Integrating, advocating and augmenting palliative care in Malaysia: a qualitative examination of the barriers faced and negotiated by Malaysian palliative care non-governmental organisations
Bibliographic record
Abstract
# Background Since its introduction in 1991, Malaysian palliative care has made significant progress, with an estimated 26 non-governmental organisations (NGOs) and 68 government hospitals providing palliative care facilities and services nationwide. Distinct models between these sectors create unique challenges for each sector in progressing palliative care, requiring different strategies to address these. # Methods Drawing on existing literature available on palliative care in Malaysia and interviews from 10 management and healthcare staff of Malaysian palliative care NGOs, this article casts new light on the field. Specifically, the paper explores the various health-related and policy-related challenges NGOs have identified in progressing palliative care in the country, as well as the current and future strategies they employ to address these. # Results Despite immense progress in Malaysian palliative care, existing services cannot meet the current and projected demand. The interviews identify numerous barriers hindering Malaysian palliative care, including financial matters, perception issues, logistical concerns and challenging government policies. # Conclusion Increased advocacy, establishment of specialised palliative care education, and greater co-operation between different sectors are strongly recommended to help develop palliative care in Malaysia.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".