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Do socioeconomic factors and local human preference determine the hybridization of knowledge in local medical systems?

Journal of Ethnobiology and Ethnomedicine volume 20, Article number: 76 (2024) Cite this article

Abstract

Background

Hybridization between the local medical systems (LMSs) and biomedicine has been the focus of different studies in ethnobiology, primarily due to the increasing access to biomedicine by indigenous peoples and local communities. Studies on hybridization allow for an understanding of the process of developing and evolving local knowledge systems. In this study, we propose a hybridization score to determine how individuals’ socioeconomic characteristics and preference between LMS and biomedicine determine the complementarity of therapeutic options.

Methods

We conducted semistructured interviews and applied free listing technique in a rural community in Northeast Brazil to assess the treatments the local population sought and which were preferred.

Results

Our analyses showed that the level of schooling was the socioeconomic factor that negatively affected the hybridization process. Individuals with higher levels of schooling tended to prefer LMS strategies less and, consequently, showed a lower probability of hybridizing the two systems. Additionally, older people who preferred LMS strategies showed a greater tendency to adopt hybridization in human health-seeking behavior.

Conclusions

Our findings provide further evidence of the complementarity between different medical systems and demonstrate that socioeconomic factors can affect local knowledge and are responsible for differences in individual propensity to hybridize distinct medical systems.

Background

Across its evolutionary trajectory, human species inhabit various and complex ecosystems where survival and reproduction are conditioned by the construction and acquisition of knowledge [1], such as knowledge and practices related to health prevention, maintenance, and care, which gives rise to complex local medical systems (LMSs) constructed by our species [2]. These knowledge systems are biocultural entities, as they have a strong influence from culture and biological factors [3] and involve the use of various resources as therapeutic options, such as animals, plants, and mineral products [2, 4], among many other natural resources. In different contexts, LMSs are not isolated and can coexist and interact with other medical systems, such as biomedicine [see 5, 6]. Biomedicine is also called a cosmopolitan medical system that, among many peculiarities, is based on treatment with industrially manufactured drugs commercialized on a large scale [2]. The coexistence of these two systems in the same space and time gives rise to hybridization scenarios [see 7].

Hybridization is a complex process and investigates how different medical systems interact with each other, which is important for our understanding of the structuring, dynamics, functioning, sustainability and evolution of LMSs. Hybridization can occur between different LMSs, and between LMS and biomedicine. Hybridization between different LMSs can occur when different knowledge and practices from different LMSs are employed in the same space, and is very common in cases of migration where people take plants from their area of origin. Hybridization between LMSs and biomedicine occurs when people use both locally sourced options and biomedical drugs to deal with illness, and is very common in cases where plants and biomedical drugs purchased from pharmacies are used [7].

For many authors, access to biomedicine by indigenous peoples and local communities (IPCL) represents a threat to local knowledge, as it gradually promoted the substitution of LMS treatment options for biomedical drugs [e.g., 8, 9]. Conversely, other evidence supports the argument that these two medical systems can interact in a complementary manner, often being employed sequentially [e.g., 5, 6, 10–14]. In this understanding, if adopting biomedicine does not interfere with local health practices, its acceptance of LMS practices can be advantageous, as it increases treatment options [15], ensuring greater therapeutic safety into LMSs. In this way, from an evolutionary perspective, the interaction between LMSs and biomedicine can be adaptive as long as it contributes to an increase in the biological and cultural fitness of the population [16].

In this context, it is necessary to understand the factors that influence interactions between different medical systems. Gender can be a reliable predictor [17], since in many small subsistence societies, women are the first to act in seeking a cure [18], generally prioritizing products of LMSs [19, 20]. Age, level of schooling, and occupation (related or not related to nature) can also influence associations between local practices and the use of biomedical drugs in different contexts [6, 21,22,23]. Other explanatory factors, such as monthly income, although not employed in hybridization contexts, follow the same pattern as the level of schooling, as they are often dependent on this variable [21, 23]. Therefore, human health-seeking behaviors may vary depending on each individual’s characteristics within the social/cultural context in which they live and interact. In addition to socioeconomic factors, individual preferences can influence the probability of a person knowing and using different treatments or restricting themselves to strategies from only one medical system. In some contexts, local practices are preferred over biomedicine, possibly due to their perceived safety and reliability [24,25,26]. In these situations, distrust in biomedicine reflected in the preference for LMSs can hinder hybridization, even in a complementary way.

Although much evidence suggests that economic variables are relevant for the interaction between different medical systems, this is the first ethnobiological approach in which different and multiple option of the LMS (such as plants, animals, human and mineral products and mystical-religious practices, among others) and their interaction with the use of biomedical drugs are considered to understand the hybridization between these two medical systems. Considering the different elements that make up SMLSs and their interaction with biomedicine is important in allowing us to track the different levels of hybridization between different medical systems. Many ethnobotanical studies have evaluated the role of socioeconomic variables and preference only in LMS knowledge without considering whether these variables also interfere in the scenario of medical system hybridization. Taking an integrated approach, the objectives of the present study were to answer the following questions: what is the role of socioeconomic (gender, age, level of schooling, nature of occupation, and individual monthly income) and the preference for LMS health treatments in structuring hybridization of local knowledge in human health-seeking behaviors? What is the role of the same socioeconomic variables on the preference for LMS health treatments over the use of biomedical drugs? To answer these questions, we used an LMS model in a rural community in the Brazilian semiarid region.

Methods

Study area

This fieldwork was conducted in the Franco rural community, municipality of Cocal (Fig. 1) (03° 28′ 15″ S–41° 33′ 18″ W), northern Piauí, Brazil, located 268 km from the state capital, Teresina. The municipality has a population of 26.036 inhabitants and a population density of 20.51 inhabitants/km2 [27]. According to the Köppen classification, the climate is Aw’ Tropical, characterized by two well-defined seasons: rainy summers and dry winters, with temperatures ranging between 25 and 35 °C. The average annual precipitation is 900 mm [28]. The Caatinga is the predominant vegetation in the Franco rural community and surrounding regions.

Fig. 1
figure 1

Location of Franco rural community, Cocal, northern Piauí state, Brazil

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The community was composed of 125 inhabitants, distributed among 35 family units. The residents mainly rely on family agriculture, cultivating primarily milho (Zea mays L.), feijão (Phaseolus vulgaris L. and varieties), macaxeira or mandioca (Manihot esculenta Crantz). In addition, they raise small, medium, and large animals, such as chickens (Gallus gallus domesticus), ducks (Cairina moschata momelanotus), goats (Capra aegagrus hircus), sheep (Ovis aries), pigs (Sus scrofa domesticus), and cattle (Bos taurus). Apart from activities related to farming, four families also engaged in small local businesses. In two local businesses, people can quickly obtain biomedical drugs for general health issues such as flu, fever, and headache. Some people sporadically engage in hunting and fishing. Many supplement their income by foraging for honey from bee colonies (Apis mellifera) in areas near and far from the community. Home garden management practices characterize the Franco rural community [29], where they cultivate a high diversity of species, especially for food and medicinal purposes, enhancing local food security and sustainability [30]. In most cases, women are responsible for the care and maintenance of these spaces [31], although the men of this community also play an essential role in maintaining knowledge associated with home gardens. The local people also collect medicinal plants from different areas, including anthropized areas, which may include roadside edges, spaces between residences, and areas of primary and secondary forests.

There are no schools in the area, so students need to move to other rural areas or to urban areas, aided by public transportation. The community does not have a Basic Health Unit or Health Unity Center. Residents are attended to by two community health agents; however, they are in irregular and/or deficient services, according to local perceptions. Therefore, people move to other rural communities for medical consultations and vaccination campaigns. In urgent cases, they visit the hospital in the urban area or are sent to the municipality of Parnaíba, 90.5 km from Cocal.

Data collection

The fieldwork was conducted from December 2019 to April 2021. To ensure greater quality of our collected data, we initially applied a pilot test with 22 local residents (16 men and 6 women) to validate and make possible adjustments to our data collection protocols [32]. The data collection protocols underwent adjustments, and due to this, the data collected in this stage were not included in the data analysis to avoid compromising its quality. The pilot test followed all the guidelines described in the ethical and legal aspects section.

Following the pilot test, to characterize the profile of the research participants, semistructured interviews [33, 34] were conducted with 48 local residents (30 women and 18 men) aged between 18 and 86 years (mean of 41 years), including data on age, gender, level of schooling, individual monthly income, and nature of occupation (number of activities related and unrelated to nature). In general, the research participants had a few years of schooling, with individual monthly incomes ranging from zero to two minimum wages. Most of those earning less than one minimum wage income come from agricultural practices, the commercialization of local products, or activities unrelated to nature. Those earning between one and two minimum wages are retirees or receive some benefit from the federal government. Among the participants, the majority followed the Catholic religion (n = 46). We interviewed all residents over 18 years old who agreed to participate in the research, representing 68.57% of the population (Table 1). This sample represents the population and their behaviors related to health practices involving the hybridization between LMS and biomedicine, according to the reasons described in Table 1.

Table 1 Population data and sample universe for conducting research in the Franco rural community, Cocal, Piauí, Northeastern Brazil
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The free listing technique was applied to collect data on memory LMS options and their interaction with the use of biomedical drugs [34, 35]. Participants were individually invited to list the known and/or used medicinal plants. After the conclusion of the free listing, we applied semistructured interviews [33] to collect complementary data about each item of the LMS recalled in the free listing, based on the following question to guide the application of technique: For which disease(s) or health problem(s) do you know and/or use this plant? and What does the person feel (symptoms) when experiencing this disease or health problem? At this stage, special care was taken to thoroughly document the therapeutic target and differentiate it from general symptoms to avoid underestimating or overestimating our constructed models. For each therapeutic target, participants were asked if, in addition to using plants, there would be another treatment option. From the individual responses, stimulus techniques were applied to identify options such as biomedical drugs, mystical-religious practices, human products, minerals, and animals.

In the second stage, we asked participants to mention health problems treated only with biomedical drugs. For each listed option, we always asked if there would be another option to treat the same disease, applying stimuli to facilitate information recall by local people. In these procedures, we aimed to document as many treatment options as possible for the same disease and to better characterize the interactions between LMS and biomedicine.

In the cases in which therapeutic targets where participants mentioned options from both LMS and biomedicine origin, we investigated the preference between using options from both systems. We considered preference to be the human choice of one therapeutic option over others that are also offered and could be used equally [36]. Participants were asked which options were preferred if they were experiencing the mentioned health problem. We recorded the individual responses in writing, which included (1) preference for LMS options over biomedical drugs, (2) preference for biomedical drugs over LMS options, and (3) sequential use of LMS and biomedicine options or vice versa during the same disease cycle experienced.

Collection, identification, and taxonomic treatment of botanical species

The collection and processing of botanical material followed the guidelines proposed by Santos et al. [37]. For their identification, consultations were made with specialized literature, visits to online databases, and the collection of the HDELTA-UFDPar Herbarium, using dichotomous keys and consulting botanical family specialists [38]. Botanical synonyms were updated using the online database of Flora e Funga do Brasil [39]. For some species not found in this database, the World Flora Online database was used (http://www.worldfloraonline.org/). The botanical families are organized alphabetically, following the proposal and taxonomic treatment of the Angiosperm Phylogeny Group IV [40], except for the Turneraceae Kunt ex DC. family, which was not considered a subfamily of Passifloraceae Juss. ex Roussel. The botanical material voucher was incorporated into the collection of the R Herbarium, Museu Nacional/UFRJ (R Herbarium), with duplicates sent to the HDELTA Herbarium, Universidade Federal do Delta do Parnaíba (UFDPar).

Identification and taxonomic treatment of zoological species

The animals indicated as medicinal resources were identified using photographs, illustrated guide analysis, and consultations with specialists in different classes. We updated the nomenclature using data available in the following databases: reptiles in The Reptile Database (https://reptile-database.reptarium.cz/), birds in Birds of the World (https://birdsoftheworld.org/bow/home), insects, arachnids, gastropods, and mammals in iNaturalist (https://www.inaturalist.org/home).

Data analysis

The collected information was compiled, categorized, and standardized into a digital database. We considered gender (m for male and f for female), age, level of schooling, individual monthly income (transformed logarithmically), and total occupations related to nature (e.g., farmer, fisherman, hunter, home garden maintainer, and house care) and unrelated to nature (e.g., mason, carpenter, and mason’s assistant) as socioeconomic variables. To estimate each participant’s preference for different medical strategies, we separated therapeutic targets where the participant indicated both local and biomedical options. The level of individual preference (P) for the LMS was calculated as the ratio of the “total therapeutic targets treated preferentially by LMS strategies” divided by the “total therapeutic targets in which strategies from both systems are indicated.” This ratio was then multiplied by 100. A higher P value indicates a greater preference for LMS options over biomedicine.

To quantify each participant’s status in a hybridization context, a score (IS) was calculated, defined as the ratio of the “total therapeutic targets in which elements from both systems are indicated” divided by the “total therapeutic targets mentioned.” This ratio was then multiplied by 100. For example, for an individual who mentioned 20 therapeutic targets treated only with medicinal plants, 3 treated with mystical-religious practices, and 21 treated with animals, plants, or biomedical drugs, the IS was 21/(21 + 3 + 20) × 100 = 52.5. The maximum value that IS can obtain is 100. The closer it is to this value, the more the individual indicates local and biomedical strategies for most therapeutic targets they know. The IS, then, assesses each individual’s contribution to generating more significant interactions between elements of different systems in a hybridization context.

To answer questions 1 (what is the role of socioeconomic (gender, age, level of schooling, nature of occupation, and individual monthly income) and the preference for LMS health treatments in structuring hybridization of local knowledge in human health-seeking behaviors?) and 2 (What is the role of the same socioeconomic variables on the preference for LMS health treatments over the use of biomedical drugs?), we checked whether the explanatory variables presented a normal distribution using the Shapiro‒Wilk test, which is recommended for small samples [41]. For both hypotheses, multiple regressions were employed; however, before developing the model, the correlation between the explanatory variables was evaluated using the Spearman test. One of these variables was excluded from the same model if a correlation was identified. For both response variables (IS and preference), several models with different combinations of predictor variables were constructed. These models were compared with a null model, from which we selected those most essential for explaining IS and preference values (data variance), including the null model, based on the Delta AIC—Akaike Information Criterion values, Δi. Lower AIC values indicate that less information was lost and that the model fit better [42]. When comparing models to the best-fitting model, Δi values < 2 suggest significant evidence for the model, Δi values between 3 and 7 indicate weak support for the model, while Δi values > 10 demonstrate that the model is improbable [43]. For all experiments, the significance level considered was p ≤ 0.05. All analyses were performed using R Software version 4.0.0 [44].

Ethical and legal procedures

This study was approved by the Research Ethics Committee (REC) of the Universidade Federal do Rio de Janeiro (Ethical Approval number 3.912.909). Meetings were held with local residents to clarify the methodological procedures to be carried out, objectives, importance, benefits, and associated risks [45]. All participants read and signed the Informed Consent Form [46, 47]. The results of this research were registered in the Sistema Nacional de Gestão do Patrimônio Genético e do Conhecimento Tradicional Associado (SISGEN) under Registration Number AD5B2EC.

Results

Elements of the LMS and their interaction with biomedical drugs

In the Franco rural community, there are multiple options of different origins to prevent, alleviate, treat, and cure many therapeutic targets (Fig. 2), in which plants (n = 189) being the most prominent, distributed across 150 genera belonging to 65 botanical families (Supplementary Material). Additionally, there are mystical-religious practices (n = 87), animals (n = 18), human products (n = 4), cosmetics and minerals (n = 4), and other options not defined in the categories mentioned above (n = 6). A total of 217 therapeutic targets were described, of which 197 (90.8%) individuals were aware of and/or used both biomedical drugs and LMS options (Supplementary Material); thus, these therapeutic targets are hybrids.

Fig. 2
figure 2

Some options of LMS and interaction with biomedical drugs: a younger people harvesting medicinal plants on forest, b medicinal preparation of Lagenaria siceraria, c Sesamum indicum collected and processed, d, e multiple medicinal complexes plants, f older people processing Ricinus communis seeds, g event of harvesting Scoparia dulcis roots, h Ricinus communis, i older people processing Allium sp., j stage of filtering an important medicinal complex plant, k event of harvesting barks of Ximenia americana, l some biomedicinal drugs which are kept for emergency events, m medicinal animal product, n event of harvesting of Croton grewioides, o event of mystical-religious practices

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Do socioeconomic variables explain the preference for local therapeutic options and determine the level of knowledge of hybridization?

Among the analyzed models, age (model weight = 0.1606, dIACc = 0.3, R2 = 0.16, t-value = 2.25, p = 0.02), level of schooling (model weight = 0.1880, dIACc = 0, R2 = 0.14, t-value = − 3.02, p = 0.004), and preference for local therapeutic options over biomedicine (model weight = 0.0625, dIACc = 2.2, R2 = 0.10, t-value = 2.15, p = 0.03) were found to be essential factors in explaining the values of knowledge of hybridization (Table 2). Age and preference positively explain the values of hybridization in LMS, while the level of schooling defines it negatively. Gender (model weight = 0.1606, dIACc = 0.3, R2 = 0.16, t-value = − 1.54, p = 0.12), income (model weight = 0.0246, dIACc = 4.1, R2 = 0.07, t-value = 0.89, p = 0.37), and occupations related (model weight = 0.0625, dIACc = 2.2, R2 = 0.10, t-value = 1.62, p = 0.11), and unrelated to nature (model weight = 0.0235, dIACc = 4.2, R2 = 0.06, value = − 0.84, p = 0.4) did not explain the differences in knowledge of hybridization between the two medical systems (Table 2).

Table 2 Relationships between socioeconomic variables and preference for local therapeutic options (explanatory variables) and the hybridization score (response variable) in the Franco rural community, Cocal-PI, Brazil
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Do socioeconomic variables influence the preference for local therapeutic options over biomedicine?

Among the models analyzed, only the level of schooling negatively affected individuals’ preference between the use of local options and biomedicine for the treatment of therapeutic targets (model weight = 0.3571, dAICc = 0, R2 = 0.75, t-value = − 2.19, p = 0.03) (Table 2). In the other models constructed, gender (model weight = 0.0644, dIAcc = 3.4, R2 = 0.006, t-value = 1.14, p = 0.25), age (model weight = 0.106, dIACc = 2.4, R2 = 0.02, t-value = 1.51, p = 0.13), individual monthly income (model weight = 0.0542, dIAcc = 3.8, R2 = 0.0005, t-value = 0.98, p = 0.32), and occupations related to nature (model weight = 0.0347, dIACc = 4.7, R2 = − 0.01, t-value = 0.32, p = 0.74) and unrelated to nature (model weight = 0.0717, dIACc = 3.2, R2 = 0.01, t-value = − 1.2, p = 0.22) did not predict the preference for the LMS option over the use of biomedical drugs (Table 3).

Table 3 Relationships between socioeconomic variables (explanatory variables) and preference for local therapeutic options (response variable) in the Franco rural community, Cocal-PI, Brazil
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Discussion

Elements of the LMS and its interaction with biomedical drugs

Our data collected from the Franco rural community suggest that individuals recognize and perceive the LMS and biomedicine as distinct but complementary medical systems. According to local perception, the LMS is seen as traditional and biomedicine as a modern medical system. The high proportion of therapeutic targets treatable by both systems suggests that hybridization is a common local phenomenon, increasing individuals’ alternatives to deal with health disorders in the same space and time. The process of hybridization resembles the process of diversification of local knowledge. In this sense, Alencar et al. [48] concluded that exotic species introduce different chemical compounds into an LMS and, in some cases, fill “therapeutic gaps” that native plants cannot satisfy. The same authors concluded that including exotic species does not threaten traditional knowledge by substituting for local species but rather diversifies healing alternatives, increasing the versatility of local knowledge. Thus, in principle, hybridization may represent an adaptation of human groups to environmental challenges associated with health and disease, contributing to the resilience of local communities [6, 15]. In this way, Greene [49] suggested that in this hybridization process, there must be a sharing between the logic of health and disease among different medical systems.

However, hybridizing medical systems may be a first step toward replacing the LMS with biomedicine, assuming that the latter and its healing practices are associated with a power project [see 50]. We are still determining whether biomedicine will gradually replace LMS in the studied community, leading to the abandonment of local practices in pursuit of health. Such understanding is only possible through diachronic studies [see 11]. These authors concluded that better knowledge of biomedical drugs was associated with increased knowledge of LMS resources. Santoro and Albuquerque [11] also demonstrated that less knowledge about biomedicine in specific therapeutic targets was related to a loss of knowledge about LMSs. These results suggest that (a) there is no substitution of one system for the other, and (b) the acquisition or loss of knowledge about therapeutic options from both systems occurs under the same logic. Thus, in the temporal scope in which we collected the data, the significant overlap in the use of both systems in the Franco rural community demonstrates that knowledge and use of biomedical drugs do not exclude local healthcare practices based on the structuration of a complex LMS.

Do socioeconomic variables explain the preference for local therapeutic options and determine the level of knowledge of hybridization?

Our results indicate that older individuals hybridize knowledge about therapeutic options, a process documented in other contexts [23, 51]. To explain this result, we start from the understanding that older individuals have broader knowledge of the resources that make up LMSs [6, 21] due to their accumulation across their lifespan, their connection to or trust in cultural and traditional practices, and their accessibility, understood as ease in collecting or foraging a natural resource without the need for payment [52]. Considering these aspects, Soldati and Albuquerque [53] indicate that trust in a particular treatment is crucial in transmitting local knowledge, meaning that people teach and learn more about socially tested and validated options. Given their high knowledge of local resources, older individuals are more prone to severe or chronic illnesses more frequently. These illness events may stimulate the use of biomedical drugs. Scherer and Speroni [54] observed that in rural communities in Argentina, in cases of serious illnesses, residents tend to seek treatments in biomedicine, while they resort more to medicinal plants for health problems they perceive as less severe. In this regard, Pieroni et al. [55] concluded that in situations of chronic diseases in rural communities in Italy, such as cancer and diabetes, there is a preference for biomedical treatments. Nascimento et al. [6] concluded that the chronic, severe, and frequent diseases in the community investigated tended to show greater use of different medical systems. In another context, Mathez-Stiefel et al. [56] concluded that their interviewees mentioned seeking new healing methods when self-treatment is insufficient to deal with serious illnesses, evidencing sequential and nonexclusive use based on the need to cure serious diseases.

According to research participants, many therapeutic targets, notably chronic diseases, can only be diagnosed with the help and knowledge of biomedical professionals, such as arterial hypertension, cancer, diabetes, hypercholesterolemia, and Chagas disease. Reports also indicate that many of these diseases can be treated with medicinal plants and that treatment with LMS resources is encouraged even by nurses and doctors in the community investigated. Our research did not aim to evaluate biomedical professionals’ recommendations for medicinal plants or other LMS options. However, we emphasize that, in Brazil, the government has programs encouraging this practice in the public health system, such as the National Policy and Program for Medicinal Plants and Phytotherapeutics [57]. Therefore, the characteristics of the disease, whether chronic or not, can encourage the relationship between biomedicine and local knowledge. In the field, through individual reports, we identified a series of other factors that can influence hybridization and preference between options from different medical systems. For example, the perception of effectiveness and the day period it takes for the treatment to lead to the healing process, the ease of access to the resource, since often LMS resources are available in people’s home gardens, the perception of the smell and taste of the home remedies based on the use of plants as indicators of effectiveness, and the time of day, since at night, the use of the biomedical drugs is guided by the availability of the drug indoors. However, as indicated by [51], the cost of using biomedical drugs determines the process of hybridization in elderly people. On the other hand, young people often have greater exposure to biomedicine and may be more receptive to innovations and changes in healthcare systems.

Our study started from the assumption that individual preference for local treatments could influence hybridization scenarios. In this context, if a person values the use of medicinal plants, for example, they would be more resistant to accepting biomedicine. In the investigated reality, individuals who show a preference for LMS options to treat diseases are more likely to hybridize LMS options with biomedicine, as discussed earlier. Thus, people who mentioned a greater number of LMS items, which can be considered specialists, and who have a greater preference for these options are not resistant to the entry of biomedicine into the LMS. Our result is consistent with the study conducted by Zank and Hanazaki [5], which showed that experts in medicinal plants and mystical-religious practices also know and use biomedical treatments. Thus, the high degree of hybridization in older individuals results from (a) trust and preference for local treatments experienced throughout the individual’s life and (b) the higher incidence of severe or chronic diseases, which stimulate biomedical strategies. However, the role of age in hybridization scenarios may be influenced by a series of other factors, such as access to health services and exposure to social media [58].

In addition to the chronicity of a disease, the severity and frequency of a disease may influence the hybridization of associated knowledge, possibly through memorization processes. In this way, human memory is adapted to prioritize information crucial for survival and reproduction, a phenomenon known as adaptive memory [59, 60]. This cognitive capacity has evolved due to the selective pressures experienced by early humans [61], facilitating the storage and retrieval of useful information related to dealing with predators and infections. Research in the Brazilian semiarid region revealed that communities tended to use various medicinal plants for diseases prevalent in their environment [62]. This suggests that the severity and frequency of challenges influence the human mind’s adaptive response. However, it remains unclear whether severity or frequency better predicts the retention of adaptive information. Sousa et al. [63] observed that in a traditional community, people tend to remember more common information about medicinal plants used for up to a maximum of one year. Similarly, information about more regular diseases is more remembered when associated with previous experiences [64]. Thus, if we assume, as indicated by Santoro and Albuquerque [11], that the enrichment of knowledge associated local and biomedical options, possibly hybridization, is influenced by other adaptive factors, such as memory.

Our results also indicate that the level of schooling negatively influences the interaction between the LMS and biomedicine. However, this finding differs from those of other studies. For example, Votova [65] and Nascimento et al. [6] demonstrated that the level of schooling increases the likelihood of individuals resorting to treatment options from LMSs and biomedicine. According to Votova [65], the level of schooling is associated with greater access to formal information and the ability to evaluate and use different health treatment options in many societies. According to this author, people with formal education may have access to more diverse sources of information, including educational resources, scientific literature, and social media, which can promote the adoption of conventional and complementary health practices. However, studies show antagonistic or neutral results in the relationship between the level of schooling and local knowledge, although the negative effect is the most sustained [58]. One explanation for this pattern is that individuals with higher levels of schooling tend to dedicate more time to learning academic skills at the expense of traditional local practices [66]. In our study, we believe that the level of schooling is a covariate of preference (P) rather than the main explanatory factor, which we discuss in the next section.

Do socioeconomic variables determine the preference for local therapeutic practices over biomedicine?

Our study started from the assumption that individual preference for local treatments could influence knowledge of hybridization. In this way, if a person values the use of medicinal plants more, for example, they would be more resistant to accepting biomedicine. As presented, our results indicate otherwise. However, what factors influence people’s preference for LMS options? The interviews indicate that the level of schooling negatively influences the preference for LMS options. As we highlighted, individuals with higher levels of schooling may have a broader understanding of healthcare systems and a more remarkable ability to know different medical systems [6, 65]. If education diminishes the preference for LMS options over biomedicine, we need to emphasize and reinforce the importance of the school environment for transmitting knowledge related to natural resources [see 21, 67]. The school environment can play an essential role in the transmission of knowledge associated with plants [68], contributing to the valorization and encouragement of these resources from a young age, especially in areas undergoing constant changes that require the development of new strategies to conserve biocultural diversity related to the domain of plant use [69] or other options inserted in the context of LMSs.

Different factors influence people’s preference for the use of medicinal plants. The perception of the medicinal properties of a plant, including its efficacy in treating specific diseases, plays a crucial role in the decision to use it [70]. Additionally, cultural beliefs and traditional practices play a significant role and are often transmitted from generation to generation and between different generations [71]. The perception of the safety and efficacy of medicinal plants, along with sensory aspects such as taste and smell, are also essential factors that guide people’s choices in the use of medicinal plants [72].

Limitations of the study

Our study evaluates the role of a series of variables in constructing interaction scenarios between the LMS and the biomedical system. The main limitation of our study is related to the fact that our analyses and approach are based only on hybrid therapeutic targets, therefore, we focused only on therapeutic targets for which both LMS and biomedical system options are indicated. This approach gives less importance to the set of therapeutic targets in which people use only LMS options and another set in which individuals exclusively use biomedical drugs, even though our analyses capture the number of total targets mentioned by each participant to calculate our hybridization score. The inclusion of all therapeutic targets would be important to expand our knowledge associated with hybridization in LMSs by allowing us to track and analyze the therapeutic targets present in the LMS, in which there is a greater predisposition for the entry and action of biomedicine.

Another important point is that our approach is punctual or focused on an analysis of a single period and LMSs are dynamic and can have their structure and functionality changed in the short term, which would be relevant to conduct longitudinal studies to expand our understanding of how LMSs react and behave in the face of biomedicine in the long term. Furthermore, we present the number of LMS options as a therapeutic resource and do not document the number of biomedical drugs used for each therapeutic target, which could expand our understanding of the levels of interaction between these two systems.

Conclusion

Our findings contribute to the existing complementarity between LMSs and biomedicine. The extent to which such systems can complement each other varies individually according to socioeconomic factors, positively by age, individual preference for LMS over biomedical drugs, and negatively by level of schooling. Interviews suggest that the severity of diseases increases the level of hybridization. Thus, hybridization between LMS and biomedicine is a phenomenon of local knowledge systems that allows greater adaptability and expands versatility in healing processes.

Availability of data and materials

All data generated or analyzed during this investigation are included in this published article and its supplementary information files. No datasets were generated or analyzed during the current study.

Abbreviations

IS:

Hybridization score

LMSs:

Local medical systems

P:

Preference

REC:

Research Ethic Committee

SISGEN:

Sistema Nacional de Gestão do Patrimônio Genético e do Conhecimento Tradicional Associado

References

  1. Pretelli I, Mulder MB, McElreath R. Rates of knowledge learning in Pemba, Tanzania: implications for childhood evolution. Evolut Hum Sci. 2022;4:1–16. https://doi.org/10.1017/ehs.2022.31.

    Article  Google Scholar 

  2. Dunn F. Traditional Asian medicine and cosmopolitan medicine as adaptive systems. In: Leslie C, editor. Asian medical systems: a comparative study. California: University California Press; 1976. p. 133–58.

    Chapter  Google Scholar 

  3. Ferreira-Júnior WS, Albuquerque UP. A theoretical review on the origin of medicinal practices in humans: echoes from evolution. Ethnobiol Conserv. 2018;7(3):1–7. https://doi.org/10.15451/ec2018-02-7.03-1-7.

    Article  Google Scholar 

  4. Kleinman A. Concepts and a model for the comparison of medical systems as cultural systems. Soc Sci Med Part B Med Anthropol. 1978;12:85–93. https://doi.org/10.1016/0160-7987(78)90014-5.

    Article  CAS  Google Scholar 

  5. Zank S, Hanazaki N. The coexistence of traditional medicine and biomedicine: a study with local health experts in two Brazilian regions. PLoS ONE. 2017;12(4):1–17. https://doi.org/10.1371/journal.pone.0174731.

    Article  CAS  Google Scholar 

  6. Nascimento ALB, Medeiros PM, Albuquerque UP. Factors in hybridization of local medical systems: simultaneous use of medicinal plants and modern medicine in Northeast Brazil. PLoS ONE. 2018;13(11):1–14. https://doi.org/10.1371/journal.pone.0206190.

    Article  CAS  Google Scholar 

  7. Ladio AH, Albuquerque UP. The concept of hybridization and its contribution to urban ethnobiology. Ethnobiol Conserv. 2014;3(6):1–9. https://doi.org/10.15451/ec2014-11-3.6-1-9.

    Article  Google Scholar 

  8. Ngokwey N. Home remedies and doctors’ remedies in Feira (Brazil). Soc Sci Med. 1995;40(8):1141–53. https://doi.org/10.1016/0277-9536(94)00241-K.

    Article  PubMed  CAS  Google Scholar 

  9. Saethre EJ. Conflicting traditions, concurrent treatment: medical pluralism in remote aboriginal Australia. Oceania. 2007;77:95–110. https://doi.org/10.1002/j.1834-4461.2007.tb00007.x.

    Article  Google Scholar 

  10. Giovanni P, Reyes-García V, Waldteins A, Heinrich M. Do pharmaceuticals displace local knowledge and use of medicinal plants? Estimates from a cross-sectional study in a rural indigenous community. Mexico Soc Sci Med. 2011;72:928–36. https://doi.org/10.1016/j.socscimed.2011.01.007.

    Article  Google Scholar 

  11. Santoro FR, Albuquerque UP. What factors guide healthcare strategies over time? A diachronic study focused on the role of biomedicine and the perception of diseases in the dynamics of a local medical system. Acta Bot Brasilica. 2020;34(4):720–9. https://doi.org/10.1590/0102-33062020abb0002.

    Article  Google Scholar 

  12. Ferrera I, Molares S, Hechem V. Intermedicality in official health contexts: use of medicinal plants by the inhabitants of a city in Argentinean Patagonia. Indian J Tradit Knowl. 2022;21(1):145–56.

    Google Scholar 

  13. Casagrande A, Ritter MR, Kubo RR. Conhecimento tradicional sobre plantas medicinais e intermedicalidade em ambientes urbanos: estudo de caso em uma comunidade popular no sul do Brasil. Ethnobot Res Appl. 2023;25(57):1–39. https://doi.org/10.32859/era.25.57.1-39.

    Article  Google Scholar 

  14. Santos SS, Santoro FR, Ferreira-Júnior WS. New evidence regarding the role of previous disease experiences on people’s knowledge and learning of medicinal plants and biomedical drugs. Ethnobot Res Appl. 2023;25(36):1–23. https://doi.org/10.32859/era.25.36.1-23.

    Article  Google Scholar 

  15. Ferreira-Júnior WS, Nascimento ALB, Ramos MA, Medeiros PM, Soldati GT, Santoro FR, Reyes-García V, Albuquerque UP. Resilience and adaptation in socio-ecological systems. In: Albuquerque UP, Medeiros PM, Casas A, editors. Evolutionary ethnobiology. Switzerland: Springer; 2015. p. 63–84.

    Google Scholar 

  16. Santoro FR, Nascimento ALB, Ferreira-Júnior WS, Soldati GT, Albuquerque UP. Teoria da evolução cultural: fundamentos. In: Albuquerque UP, Ferreira-Júnior WS, editors. Fundamentos de Etnobiologia Evolutiva. Recife: NUPEEA; 2018. p. 59–96.

    Google Scholar 

  17. Kristoffersen AE, Stub T, Salamonsen A, Musial F, Hamberg K. Gender differences in prevalence and associations for use of CAM in a large population study. BMC Complement Altern Med. 2014;14(463):1–9. https://doi.org/10.1186/1472-6882-14-463.

    Article  Google Scholar 

  18. Siqueira JIA, Machado TJ, Lemos JR. Bioculturalidad asociada a la extracción y uso del aceite de una etnovariedad de Sesamum L. (Pedaliaceae): un abordaje etnobotánico en un pueblo del semiárido de Piauí (Noreste de Brasil). Ethnobot Res Appl. 2019;18(2):1–26. https://doi.org/10.32859/era.18.17.1-26.

    Article  Google Scholar 

  19. Al-Krenawi A, Graham JR. Gender and biomedical/traditional mental health utilization among the Bedouin-Arabs of the Negev. Cult Med Psychiatry. 1999;23:219–43. https://doi.org/10.1023/A:1005455809283.

    Article  PubMed  CAS  Google Scholar 

  20. Adams WA. The interface between medical anthropology and medical ethnobiology. J R Anthropol Inst. 2006;12(1):95–118.

    Google Scholar 

  21. Santoro FR, Richeri M, Ladio AH. Factors affecting local plant knowledge in isolated communities from Patagonian steppe: metacommunity theory is revealed as a methodological approach. PLoS ONE. 2022;17(9):1–21. https://doi.org/10.1371/journal.pone.0274481.

    Article  CAS  Google Scholar 

  22. Grzywacz JG, Quandt SA, Neiberg R, Lang W, Bell RA, Arcury TA. Age-related differences in the conventional health care-complementary and alternative medicine link. Am J Health Behav. 2008;32(6):650–63. https://doi.org/10.5555/ajhb.2008.32.6.650.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Shih CC, Su YC, Liao CC, Lin JG. Patterns of medicinal pluralism among adults: results from the 2001 National Health Interview Survey in Taiwan. BMC Health Serv Res. 2010;10(191):1–9. https://doi.org/10.1186/1472-6963-10-191.

    Article  Google Scholar 

  24. Linnar-Palmer L, Kools S. Parents’ refusal of medical treatment based on religious and/or cultural beliefs: the law, ethical principles, and clinical implications. J Pediatr Nurs. 2004;9(5):351–6. https://doi.org/10.1016/j.pedn.2004.05.014.

    Article  Google Scholar 

  25. Birhan W, Giday M, Teklehaymanot T. The contribution of traditional healers’ clinics to public health care system in Addis Ababa, Ethiopia: a cross-sectional study. J Ethnobiol Ethnomed. 2011;7(39):1–7. https://doi.org/10.1186/1746-4269-7-39.

    Article  Google Scholar 

  26. Tuasha N, Petros B, Asfaw Z. Medicinal plants used by traditional healers to treat malignancies and other human ailments in Dalle District, Sidama Zone, Ethiopia. J Ethnobiol Ethnomed. 2018;14(15):1–21. https://doi.org/10.1186/s13002-018-0213-z.

    Article  Google Scholar 

  27. Ibge. Instituto Brasileiro de Geografia e Estatística. 2010. http://cidades.ibge.gov.br/painel/painel.php?codmun=220200. Accessed 10 May 2021.

  28. Medeiros RM. Estudo agrometeorológico para o estado do Piauí. Teresina: Sema; 2004.

    Google Scholar 

  29. Siqueira JIA, Vieira IR, Chaves EMF, Sanabria-Diago OL, Lemos JR. Biocultural behavior and traditional practices on the use of species of Euphorbiaceae in rural home gardens of the Semiarid Region of Piauí State (NE, Brazil). Caldasia. 2020;42(1):70–84. https://doi.org/10.15446/caldasia.v42n1.76202.

    Article  Google Scholar 

  30. Siqueira JIA, Lemos JR. Conhecimento dos quintais agroflorestais na comunidade rural Franco, Cocal, Piauí. In: XXV Seminário de Iniciação Científica, UFPI, 2016, Teresina, Piauí, Anais […]. do XXV Seminário de Iniciação Científica da UFPI. Teresina: EDUFPI; 2016. p. 1–3.

  31. Siqueira JIA, Chaves EMF, Lemos JR. Ethnobotanical study on the use medicinal plants in agroforestry backyards in the environmental protection area of the “Serra da Ibiapaba”, Northeastern Brazil. In: Mathias A, Laisné N, editors. Medicinal plants: production, cultivation and uses. New York: Nova Science Publishers; 2017. p. 211–30.

    Google Scholar 

  32. Silva TLL, Moura JMB, Hora JSL, Oliveira ES, Sousa AS, Silva NA, Albuquerque UP. Implementing ethnobiological research: pretests, quality control, and protocol reviews. In: Albuquerque UP, Lucena RFP, Cunha LVFC, Alves RRN, editors. Methods and techniques in ethnobiology and ethnoecology. 2nd ed. New York: Springer; 2019. p. 15–24.

    Google Scholar 

  33. Martin GJ. Ethnobotany: a methods manual. London: Chapman and Hall; 1995.

    Book  Google Scholar 

  34. Albuquerque UP, Ramos MA, Lucena RFP, Alencar NL. Methods and techniques used to collect ethnobiological data. In: Albuquerque UP, Lucena RFP, Cunha LVFC, Alves RRN, editors. Methods and techniques in ethnobiology and ethnoecology. New York: Springer; 2014. p. 15–38.

    Chapter  Google Scholar 

  35. Bernard HR. Research methods in anthropology: qualitative and quantitative approaches. 4th ed. Oxford: Altamira Press; 2006.

    Google Scholar 

  36. Albuquerque UP, Andrade LDHC, Silva ACO. Use of plant resources in a seasonal dry forest (Northeastern Brazil). Acta Bot Brasilica. 2005;19:27–38. https://doi.org/10.1590/S0102-33062005000100004.

    Article  Google Scholar 

  37. Santos LL, Vieira FJ, Nascimento LGS, Silva ACO, Santos LL, Sousa GM. Techniques for collecting and processing plant material and their application in ethnobotany research. In: Albuquerque UP, Lucena RFP, Cunha LVFC, Alves RRN, editors. Methods and techniques in ethnobiology and ethnoecology. New York: Springer; 2014. p. 161–73.

    Chapter  Google Scholar 

  38. Silva RCVM, Silva ASL, Fernandes MM, Margalho LF. Noções morfológicas e taxonômicas para identificação botânica. Brasília: EMBRAPA; 2014.

    Google Scholar 

  39. Flora e funga do Brasil. Lista de Espécies da Flora do Brasil. 2024. https://floradobrasil.jbrj.gov.br/. Accessed 10 Jan 2024.

  40. Angiosperm Phylogeny Group (APG). An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG IV. Bot J Linnean Soc. 2016;181:1–20.

    Article  Google Scholar 

  41. Torman VBL, Coster R, Riboldi J. Normalidade de variáveis: métodos de verificação e comparação de alguns testes não-paramétricos por simulação. Revista HCPA. 2012;32(2):227–34.

    Google Scholar 

  42. Alvarenga AMT. Modelos lineares generalizados: aplicação a dados de acidentes rodoviários. 2015. 93 f. Dissertation’ Thesis (Mestrado em Gestão de Informação) ―Universidade de Lisboa, Lisboa

  43. Mazerolle MJ. Appendix 1: making sense out of Akaike’s information criterion (AIC): its use and interpretation in model selection and inference from ecological data. Mouvements et reproduction des amphibiens en tourbières perturbèes. 2004. p. 174–190.

  44. R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. 2020. https://cran.r-project.org/src/base/R-4/. Accessed 20 May 2021.

  45. Alexiades MN. Protocol for conducting ethnobotanical research in the tropics. In: Alexiades MN, editor. Ethnobotanical research: a field manual. New York: The New York Botanical Garden; 1996. p. 5–18.

    Google Scholar 

  46. International Society of Ethnobiology–ISE. ISE Code of Ethics (with 2008 additions). 2006. http://ethnobiology.net/code-of-ethics/. Accessed 01 Jun 2021.

  47. Brasil. Conselho Nacional de Saúde, Resolução N° 466, de 12 de Dezembro de 2012. Brasília: Diário Oficial da União; 2012.

  48. Alencar NL, Araújo TAS, Amorim ELC, Albuquerque UP. The inclusion and selection of medicinal plants in traditional pharmacopoeias—evidence in support of the diversification hypothesis. Econ Bot. 2010;64:68–79. https://doi.org/10.1007/s12231-009-9104-5.

    Article  Google Scholar 

  49. Greene S. The Shaman’s needle: development, shamanic agency, and intermedicality in Aguaruna Lands. Peru Am Ethnol. 1998;25(4):634–58. https://doi.org/10.1525/ae.1998.25.4.634.

    Article  Google Scholar 

  50. Laplantine F. Antropologia da doença. São Paulo: WMF Martins Fontes; 2012.

    Google Scholar 

  51. Inoue Y, Umezaki M. Medical pluralism and traditional/complementary and alternative medicine use among older people: a cross-sectional study in a rural mountainous village in Japan. J Cross Cult Gerontol. 2016;31(1):57–72. https://doi.org/10.1007/s10823-015-9277-6.

    Article  PubMed  Google Scholar 

  52. Pfeiffer JM, Butz RJ. Assessing cultural and ecological variation in ethnobiological research: the importance of gender. J Ethnobiol. 2005;25(2):240–78. https://doi.org/10.2993/0278-0771_2005_25_240_acaevi_2.0.co_2.

    Article  Google Scholar 

  53. Soldati GT, Albuquerque UP. Does environmental instability favor the production and horizontal transmission of knowledge regarding medicinal plants? A study in southeast Brazil. PLoS ONE. 2015;10(5):1–16. https://doi.org/10.1371/journal.pone.0126389.

    Article  CAS  Google Scholar 

  54. Scherer R, Speroni CM. Ethnobotanical approach of traditional medicine in Central Argentina: an empirical method for the selection of medicinal plants. J Ethnopharmacol. 2017;202:196–207.

    Google Scholar 

  55. Pieroni A, Quave CL, Villanelli ML, Mangino P, Sabbatini G, Santini L, Boccetti T, Profili M, Ciccioli T, Rampa LG, Antonini G, Girolamini C, Cecchi M, Tomasi M. Ethnopharmacognostic survey on the natural ingredients used in folk cosmetics, cosmeceuticals and remedies for healing skin diseases in the inland Marches, Central-Eastern Italy. J Ethnopharmacol. 2004;91(2–3):331–44. https://doi.org/10.1016/j.jep.2004.01.015.

    Article  PubMed  Google Scholar 

  56. Mathez-Stiefel S, Vandebroek I, Rist S. Can Andean medicine coexist with biomedical healthcare? A comparison of two rural communities in Peru and Bolivia. J Ethnobiol Ethnomed. 2012;8(26):1–14. https://doi.org/10.1186/1746-4269-8-26.

    Article  Google Scholar 

  57. Brasil. Política Nacional de Plantas Medicinais e Fitoterápicos. Decreto No 5.813, de 22 de junho de 2006.

  58. Reyes-García V, Kightley E, Ruiz-Mallén I, Fuentes-Peláez N, Demps K, Huanca T, Martínez-Rodríguez MR. Schooling and local environmental knowledge: Do they complement or substitute each other? Int J Educ Dev. 2010;30(3):305–13. https://doi.org/10.1016/j.ijedudev.2009.11.007.

    Article  Google Scholar 

  59. Nairne JS, Thompson SR, Pandeirada JNS. Adaptive memory: survival processing enhances retention. J Exp Psychol Learn Mem Cogn. 2007;33(2):263–73. https://doi.org/10.1177/1745691616635613.

    Article  PubMed  Google Scholar 

  60. Nairne JS, Pandeirada JNS. Adaptive memory: Is survival processing special? J Mem Lang. 2008;59(3):377–85. https://doi.org/10.1177/1745691616635613.

    Article  Google Scholar 

  61. Tooby J, Cosmides L. The theoretical foundations of evolutionary psychology. In: Buss DM, editor. The handbook of evolutionary psychology. New Jersey: Wiley; 2016. p. 5–67.

    Google Scholar 

  62. Santoro FR, Ferreira-Júnior WS, Ladio AH, Araújo TAS, Albuquerque UP. Does plant species richness guarantee the resilience of local medical systems? A perspective from utilitarian redundancy. PLoS ONE. 2015;10(3):1–18. https://doi.org/10.1371/journal.pone.0119826.

    Article  CAS  Google Scholar 

  63. Sousa DCP, Soldati GT, Monteiro JM, Araújo TAS, Albuquerque UP. Information retrieval during free listing is biased by memory: evidence from medicinal plants. PLoS ONE. 2016;11(11):1–15. https://doi.org/10.1371/journal.pone.0165838.

    Article  CAS  Google Scholar 

  64. Silva RH, Moura JMB, Ferreira-Júnior WS, Nascimento ALB, Albuquerque UP. Previous experiences and regularity of occurrence in evolutionary time affect the recall of ancestral and modern diseases. Evol Psychol Sci. 2022;8:363–73. https://doi.org/10.1007/s40806-022-00325-0.

    Article  Google Scholar 

  65. Votova KME. The medical pluralism paradigm: examining patterns of the use across conventional, complementary and public health care systems among Canadian aged 50 and older. 367 f. Doctorate Thesis (Doutorado em Filosofia)—University of Victoria, Canadá; 2012.

  66. Sternberg RJ, Nokes C, Geissler PW, Prince R, Okatcha F, Bundy DA, Grigorenko EL. The relationship between academic and practical intelligence: a case study in Kenya. Intelligence. 2001;29(5):401–18. https://doi.org/10.1016/S0160-2896(01)00065-4.

    Article  Google Scholar 

  67. Sotero MC, Alves AGC, Arandas JKG, Medeiros MFT. Local and scientific knowledge in the school context: characterization and content of published works. J Ethnobiol Ethnomed. 2020;16(23):1–28. https://doi.org/10.1186/s13002-020-00373-5.

    Article  Google Scholar 

  68. van Luijk N, Soldati GT, Fonseca-Kruel VS. The role of schools as an opportunity for transmission of local knowledge about useful Restinga plants: experiences in southeastern Brazil. J Ethnobiol Ethnomed. 2021;17(34):1–13. https://doi.org/10.1186/s13002-021-00461-0.

    Article  Google Scholar 

  69. Bruyere BL, Trimarco J, Lemungesi S. A comparison of traditional plant knowledge between students and herders in northern Kenya. J Ethnobiol Ethnomed. 2016;12(48):1–10. https://doi.org/10.1186/s13002-016-0121-z.

    Article  Google Scholar 

  70. Ferreira-Júnior WS, Ladio AH, Albuquerque UP. Resilience and adaptation in the use of medicinal plants with suspected anti-inflammatory activity in Brazilian Northeast. J Ethnopharmacol. 2011;138(1):238–52. https://doi.org/10.1016/j.jep.2011.09.018.

    Article  Google Scholar 

  71. Voeks RA. Ethnobotany. In: Richardson D, Castree N, Goodchild MF, Kobayashi A, Liu W, Marston RA, editors. Internacional encyclopedia of geography. Fullerton: Wiley; 2017. p. 1–4.

    Google Scholar 

  72. Ferreira-Júnior WS, Santo FR, Albuquerque UP. Nossa história evolutiva: plantas medicinais e a origem e evolução da medicina. Recife: NUPEEA; 2018.

    Google Scholar 

Download references

Acknowledgements

Our sincere thanks to the Franco rural community for their receptiveness, collaboration and attention throughout the research. To the Conselho Nacional de Desenvolvimento Cietntífico e Tecnológico-CNPq for the research grant, master’s level, Process nº 134354/2019-2, granted to the first author.

Funding

The study did not receive funding for its execution.

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Authors and Affiliations

  1. Laboratório de Taxonomia de Angiospermas e Etnobotânica, Museu Nacional, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

    Jorge Izaquiel Alves de Siqueira & Luci de Senna Valle

  2. Laboratório Sociobiologia, Departamento de Botânica, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil

    Gustavo Taboada Soldati

  3. Universidade de Pernambuco, Nazaré da Mata, Brazil

    Washington Soares Ferreira-Júnior

  4. Laboratório de Ciências Ambientais, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, Brazil

    Flávia Rosa Santoro

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  1. Jorge Izaquiel Alves de Siqueira
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  4. Flávia Rosa Santoro
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  5. Luci de Senna Valle
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Contributions

JIAS conceptualized the design of the study and data collection, WSF-J provided the formal analyses, and GTS and LSV contributed to the project administration. JIAS and GTS organized the data. JIAS wrote the initial draft, and GT, FRS, LSV and WSF-J contributed by reviewing, writing and discussing the research results. All authors read and approved the final version.

Corresponding author

Correspondence to Jorge Izaquiel Alves de Siqueira.

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Ethics approval and consent to participate

Ethical approval of the study was obtained from the Research Ethics Committee of the Universidade Federal do Rio de Janeiro (Ethical Approval number 3.912.909). The results of the investigation were registered in the Sistema Nacional de Gestão do Patrimônio Genético e do Conhecimento Tradicional Associado-SISGEN (Registration Number AD5B2EC).

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de Siqueira, J.I.A., Soldati, G.T., Ferreira-Júnior, W.S. et al. Do socioeconomic factors and local human preference determine the hybridization of knowledge in local medical systems?. J Ethnobiology Ethnomedicine 20, 76 (2024). https://doi.org/10.1186/s13002-024-00722-8

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Journal of Ethnobiology and Ethnomedicine

ISSN: 1746-4269

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