Identifying Barriers to Effective Cancer Pain Management in Oman: Implications for Palliative Care
Bibliographic record
Abstract
BACKGROUND: Effective cancer pain management is essential for improving the quality of life of patients. However, the use of analgesics is often suboptimal due to various patient-related barriers. This study aims to explore the perceptions, knowledge, and attitudes toward analgesic use among cancer patients in Oman, which may influence their pain management strategies. METHODS: In a cross-sectional study, we assessed 68 cancer patients undergoing pain management at an inpatient cancer clinic of a tertiary hospital in Oman from a pool of 154 eligible participants. The Barriers Questionnaire (BQ) and the Patient Pain Questionnaire (PPQ), both Arabic versions, were administered to evaluate the patients' barriers to cancer pain management. The study period and the criteria for patient selection are specified. RESULTS: With a participation rate of 44.2% and a female-to-male ratio of 2.28:1, the mean score on the BQ was 2.52 (SD 0.84), indicating a moderate level of perceived barriers. Patients' scores suggested notable barriers, with older patients exhibiting reluctance toward analgesics for fear of masking symptoms and female patients expressing greater concerns about developing drug tolerance. CONCLUSION: The findings highlight significant attitudinal barriers to effective cancer pain management in Oman, notably a prevalent fear of medication tolerance. The study stresses on the need for targeted patient education and the correction of misconceptions. It also points to the influence of cultural and religious beliefs on patient responses, advocating for the implementation of culturally sensitive, evidence-based pain management guidelines, and the support of multidisciplinary palliative care teams.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".