Complementary and Alternative Medicine (CAM) use among cancer patients / Somiyaton Mohd Dahalan @ Damuri
Bibliographic record
Abstract
The Traditional and Complementary Medicine (TCM) Unit was established in three hospitals; namely, Hospital Kepala Batas in Pulau Pinang, Hospital Putrajaya in the Federal Territory of Putrajaya and Hospital Sultan Ismail· in Johor, which was approved by the Malaysian Cabinet in 2006. The unit initially provided three modalities of treatment comprising of acupuncture, Malay traditional massage and herbal therapy as adjunct treatments for cancer. Despite significant advances in treatment and the incidence of cancer in adults continuing to rise worldwide, many cancers remain incurable. One of the most feared symptoms in cancer is pain. It will affect most patients at some stage during their illness: The objectives of the study are to assess patients' beliefs and reasons on the use of Complementary and Alternative Medicine (CAM) with conventional medicine in patients with cancer, to assess the relationship between demographic factors and beliefs towards using CAM, to find the relationship between beliefs and pain and to assess patients' attitudes toward pain on CAM use in cancer. Patients (with cancer) from the (TCM) Unit Hospital Putrajaya, Hospital Kepala Batas and Hospital Sultan Ismail were identified for this study. A questionnaire on CAM usage, beliefs associated with CAM usage by Rakovitch et al., (2005) were adopted and modified to answer study objectives. Patients' beliefs on CAM usage were assessed using a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). An adapted version of the Short-form McGill Questionnaire from Melzack 1987 by Bond and Simpson (2006) was used to assess pain in cancer among patients. Taking into account that the sample of the study was not normally distributed in terms of race, gender, age and site of sample taken, non-parametric statistics was used in this study to assess the relationship between demographic factors and belief towards using CAM. Correlation between beliefs about CAM usage and pain dimension was tested using the Pearson Correlation.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".