Survey of the use of traditional and complementary medicine among children with cancer at three hospitals in Cameroon
Bibliographic record
Abstract
INTRODUCTION: There is lack of diagnostic and treatment resources with variable access to childhood cancer treatment in low- and middle-income countries (LMIC), which may lead to subsequent poor survival. The primary aim of this study was to determine the prevalence and types of traditional and complementary medicine (T&CM) used in Cameroon. Secondarily, we explored determinants of T&CM use, associated costs, perceived benefits and harm, and disclosure of T&CM use to medical team. METHODS: A prospective, cross-sectional survey among parents and carers of children younger than 15 years of age who had a cancer diagnosis and received cancer treatment at three Baptist Mission hospitals between November 2017 and February 2019. RESULTS: Eighty participants completed the survey. Median patient age was 8.1 years (IQR4.1-11.1). There was significant availability (90%) and use (67.5%) of T&CM, whereas 24% thought T&CM would be good for cancer treatment. Common T&CM remedies included herbs and other plant remedies or teas taken by mouth, prayer for healing purposes and skin cutting. Living more than five hours away from the treatment center (P = 0.030), anticipated costs (0.028), and a habit of consulting a traditional healer when sick (P = 0.006) were associated with the use of T&CM. T&CM was mostly paid for in cash (53.7%) or provided free of charge (29.6%). Of importance was the fact that nearly half (44%) did not want to disclose the use of TM to their doctor. CONCLUSION: Pediatric oncology patients used T&CM before and during treatment but were unlikely to disclose its use to the child's health care team.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".