Patient Access to Opioids for Pain Management in SEA with a Focus on Timor-Leste: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: The majority of the world's population, including the Southeast Asia region, needs legitimate access to opioids, for the treatment of moderate to severe pain. The escalating incidence of cancer in Timor-Leste, the newest nation in the region, presents a significant challenge to healthcare systems particularly in pain management. OBJECTIVES: To examine the literature on patients' access to effective pain management in Southeast Asian countries with a focus on Timor-Leste. METHODS: Four databases and records were searched on January 1, 1999 to May 15, 2025, including Scopus, MEDLINE via Ovid, Embase via Ovid, and CINAHL via EBSCO. RESULTS: Fifty studies were analyzed using the World Health Organization's (WHO) health system framework and the Levesque conceptual framework for healthcare access. Over one-third of the studies included were carried out in Thailand, and only two studies, including a report, were conducted in Timor-Leste. Approximately 70% of the studies were categorized under service delivery, workforce, and availability of medicines, financing and affordability aspects of opioids access were the least category explored. DISCUSSION: The literature revealed significant barriers impeding adequate access to pain management, including regulatory obstacles, a lack of trained healthcare workers, medications' availability, deficiencies in pain assessment, affordability, and resistance to opioid treatment among both patients and healthcare providers due to stigma and cultural beliefs. CONCLUSION: As a country that is in the process of strengthening its healthcare sector, it is crucial for Timor-Leste to understand regional research for effective intervention to improve access to pain management policies and practices.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".