Prevalence and modes of complementary and alternative medicine use among peasant farmers with musculoskeletal pain in a rural community in South-Western Nigeria
Bibliographic record
Abstract
BACKGROUND: Anecdotally, use of Complementary and Alternative Medicine (CAM) for Musculoskeletal Pain (MSP) is common in Nigeria; however, there seems to be a dearth of empirical data on its prevalence and mode of use. This study investigated the prevalence and modes of use of CAM for MSP among farmers in a rural community in South-western Nigeria. METHODS: This cross-sectional survey employed multistage sampling technique guidelines for conducting community survey by the World Health Organization among rural community farmers in Gudugbu village, Oyo State, Nigeria. A questionnaire developed from previous studies and validated by expert reviews was used to assess prevalence and modes of CAM use. Data was analyzed using descriptive and inferential statistics. Alpha level was set at p < 0.05. RESULTS: A total of 230 consenting rural farmers volunteered for this study with a valid response rate of 93.9 % (n = 216). The lifetime, 12-month and point prevalence of CAM for MSP was 96.8 % respectively. Herbal therapy and massage were the predominant types of CAM therapies among previous (83.8 and 80.1 %) and current CAM users (37.5 and 37.5 %). CAM was largely used as sole therapy for MSP (75.5 %) and also in combination with orthodox medicine (23.6 %), and it is consumed on daily basis (21.8 %). CAM was perceived to be very good in maintaining a healthy life (87.1 %) and has less side effects (74 %) and more healthy than taking doctors' prescriptions (63.4 %). CONCLUSION: There is a high prevalence of CAM among Nigerian rural farmers. The most commonly employed CAM for MSP were herbal remedies and massage which are attributable to beliefs on their perceived efficacy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".