The Perceptions of Complementary and Alternative Medicine among People with Chronic Diseases in Mwala Sub-County, Machakos County, Kenya
Bibliographic record
Abstract
Purpose: This paper sought to determine the perceptions of the nurses on complementary and alternative medicine in Machakos County, Kenya. Methodology: This was a descriptive study, it had 3 objectives: to determine the perspectives of the nurses about CAM; establish the drivers of CAM in Machakos; and to investigate the available models of CAM in Machakos. A standardized questionnaire was used to collect quantitative data from the patients with chronic diseases. Findings: the nurses who knew about CAM were 60%; those who had negative perspective about CAM were 68%, those who had used some CAM treatment were 50%; the patients with chronic diseases hesitance to use CAM was based on issues of safety, unknown effects of the treatment, lack of clear dosage, ethical, and quality. However, a few had used CAM for external only. Unique Contribution to Theory Policy and Practice: The study concluded that for the people with chronic diseases perspectives towards CAM and consequently the usage of CAM needs more research to establish the validity and reliability of such perspectives. The consumers of CAM should be assisted to access more accurate information as front-line consumers of CAM that will dispel concerns correct dosages, safety of the CAMs, their effectiveness, any risks and possible therapeutic values from CAMs in general. The study recommends more random and controlled pragmatic clinical trials and sharing of information and experiences by paramedics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".