Perspectives, Perceptions and Experiences in Postoperative Pain Management in Developing Countries: A Focus Group Study Conducted in Rwanda
Bibliographic record
Abstract
BACKGROUND: Access to postoperative acute pain treatment is an important component of perioperative care and is frequently managed by a multidisciplinary team of anesthesiologists, surgeons, pharmacists, technicians and nurses. In some developing countries, treatment modalities are often not performed due to scarce health care resources, knowledge deficiencies and cultural attitudes. OBJECTIVES: In advance of a comprehensive knowledge translation initiative, the present study aimed to determine the perspectives, perceptions and experiences of anesthesia residents regarding postoperative pain management strategies. METHODS: The present study was conducted using a qualitative assessment strategy in a large teaching hospital in Rwanda. During two sessions separated by seven days, a 10-participant semistructured focus group needs analysis was conducted with anesthesia residents at the Centre Hospitalier Universitaire de Kigali (Kigali, Rwanda). Field notes were analyzed using interpretative and descriptive phenomenological approaches. Participants were questioned regarding their perspectives, perceptions and experiences in pain management. RESULTS: The responses from the focus groups were related to five general areas: general patient and medical practice management; knowledge base regarding postoperative pain management; pain evaluation; institutional/system issues related to protocol implementation; and perceptions about resource allocation. Within these areas, challenges (eg, communication among stakeholders and with patients) and opportunities (eg, on-the-job training, use of protocols, routine pain assessment, participation in resource allocation decisions) were identified. CONCLUSIONS: The present study revealed the prevalent challenges residents perceive in implementing postoperative pain management strategies, and offers practical suggestions to overcoming them, primarily through training and the implementation of practice recommendations.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".