Complementary and Alternative Medicine Use among Gynaecological Cancer Patients in Enugu, South-East Nigeria
Bibliographic record
Abstract
INTRODUCTION: The high cost and associated adverse effects of conventional therapy make the patients seek complementary and alternative medicine. Complementary and alternative medicine use may contribute to the delay the patients have before seeking orthodox care. OBJECTIVES: To determine the prevalence, pattern, factors responsible for complementary and alternative medicine use and if complementary and alternative medicine use causes delay in seeking orthodox care among gynaecological cancer patients in Enugu. MATERIAL & METHODS: A pretested, semi-structured interviewer-administered questionnaire -based cross-sectional survey of 396 eligible gynaecological cancer patients recruited from both University of Nigeria Teaching Hospital, Enugu and Enugu State University of Science and Technology Teaching Hospital, Enugu between January, 2018 and June, 2020. The data was analyzed using the statistical package for social sciences version 23 (IBM SPSS, Chicago, IL). RESULTS: The mean age of the respondents was 57.3 years and it ranged from 41 to 77 years. Cervical cancer (76%) was the most common type of gynaecological cancer. A total of 283(71.5%) of the respondents used complementary and alternative medicine. Herbs (65%) were the most common type of complementary and alternative medicine used. Majority (56.9%) of complementary and alternative medicine users rarely used it. A total of 178(62.8%) complementary and alternative medicine users presumed that it would cure them while 105 (37.1%) presumed that it would relieve the symptoms of their cancer. However, 197 (69.6%) complementary and alternative medicine users stated that there was no actual benefit from its use. The low educational status has a statistically significant influence on complementary and alternative medicine use among the respondents(P-value=<0.0001). Complementary and alternative medicine use had a significant influence on delay to seek orthodox care among the study participants (P-value=<0.0001). Majority (84.5%) of the doctors, caring for the patients, were not aware of the patients’ use of complementary and alternative medicine. CONCLUSION: Complementary and alternative medicine use among gynaecological cancer patients is high in Enugu. It also delays their presentation for orthodox care. Female education and public sensitization on the effect of complementary and alternative medicine use is necessary to curb this trend.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".