Perceptions of complementary/alternative medicine use and influence on evidence-based asthma medicine adherence in Malaysian children
Bibliographic record
Abstract
Complementary and alternative medicine (CAM) is widely used especially in Asia including for childhood asthma. The use of CAM could influence adherence to evidence-based (E-B) medicine. We explored the views of carers of Malaysian children with asthma regarding the use of CAM for childhood asthma, and its relationship with self-reported adherence to E-B medicine. We used a screening questionnaire to identify children diagnosed with asthma from seven suburban primary schools in Malaysia. Informed consent was obtained prior to the interviews. We conducted the interviews using a semi-structured topic guide in participants' preferred language (Malay, Mandarin, or Tamil). All interviews were audio-recorded, transcribed verbatim and coded using Nvivo. Analysis was performed thematically, informed by the Necessity-Concerns Framework. A total of 46 carers (16 Malays, 21 Indians, 9 Chinese) contributed to 12 focus groups and one individual interview. We categorised participants' as 'Non-CAM'; 'CAM'; or 'combination' user. Cultural practices and beliefs in the efficacy of CAM resulted in widespread use of CAM. Most carers used CAM as 'complementary' to E-B medicine. Concerns about dependence on or side effects of E-B treatment influenced carers' decisions to rely on CAM as an 'alternative', with an important minority of accounts describing potentially harmful CAM-use. Healthcare professionals should discuss beliefs about the necessity for and concerns about use of both E-B medicine and CAM, and provide balanced information about effectiveness and safety. The aim is to improve adherence to regular E-B preventer medication and prevent delays in seeking medical advice and harmful practices associated with CAM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".