Comparing Perspectives on Traditional and Complementary Medicine Use in Oncology: Insights from Healthcare Professionals and Oncology Patients in Western Mexico
Bibliographic record
Abstract
Traditional and complementary medicine (T&CM) plays a significant role in healthcare practices among healthcare professionals and oncology patients in Mexico, reflecting its cultural importance. This study aimed to analyze the prevalence, frequency, and factors associated with T&CM use in these two groups, highlighting the differences in practices and perceptions. A total of 382 individuals participated, including 152 healthcare professionals and 230 oncology patients. The findings revealed that while T&CM use was similarly prevalent among healthcare professionals (85.7%) and oncology patients (90.8%), frequent use (≥2 times per week) was significantly higher among patients (46.3%) compared to healthcare professionals (19.1%, p < 0.001). Healthcare professionals showed a preference for non-conventional nutritional interventions (32.5%) and yoga (14.6%) while oncology patients favored plant-based remedies (73.6%) and the consumption of exotic animals and venoms (4.8%). Females were more likely to use T&CM across both groups, with a stronger association among healthcare professionals (AdOR 3.695, 95% CI 1.8–7.4). Oncology patients were less likely to understand T&CM concepts and were more commonly associated with lower socioeconomic status and educational attainment. These findings underscore the importance of considering cultural and demographic factors when integrating T&CM into conventional medical care, especially in regions where T&CM remains widely practiced and trusted.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".