Barriers and facilitators to postoperative pain management in Rwanda from the perspective of health care providers: A contextualization of the theory of planned behavior
Bibliographic record
Abstract
AIMS: Identify opportunities to improve knowledge translation for post-operative pain management in Rwanda by exploring clinician and environmental factors affecting this practice. METHODS: The theory of planned behavior (TPB) guided development of a questionnaire to measure intent to assess and treat postoperative pain. Focus groups and individual interviews were used to contextualize the final questionnaire and generate questions related to pain management practice. Health care providers from two Rwandan teaching hospitals involved in postoperative pain management completed the TPB questionnaire in May 2015. TPB subscale scores were analyzed to identify demographic and practice characteristics associated with intention to treat pain. The general linear model was used to test effect of attitudes, subjective norms, and perceived control on behavioral intent to treat pain. RESULTS: = 131) had training in acute pain management, 56% used a pain protocol, and 74% used pain scales. Tramadol (78%), morphine (79%), and paracetamol (75%) were used most often to treat pain. Drug availability was the most frequently reported barrier to treating pain. Though intention to treat pain was high, only attitudes and perceived control about assessing pain were associated with intention to treat pain. The theme of fear of the adverse effects of pain medications was consistent across focus groups and interviews in both sites. CONCLUSIONS: System and knowledge barriers exist: interventions to address these barriers may lead to improved postoperative pain care. Further validation of the TPB questionnaire is required to address cultural and language factors specific to the Rwandan context.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".