Postoperative Pain Management Among Dominican and American Health-Care Providers
Bibliographic record
Abstract
BACKGROUND: U.S. practitioners have prescribed opioid analgesics increasingly in recent years, contributing to what has been declared an opioid epidemic by the U.S. Centers for Disease Control and Prevention (CDC). Opioids are used frequently in the preoperative and postoperative periods for patients undergoing total joint replacement in developed countries, but cross-cultural comparisons of this practice are limited. An international medical mission such as Operation Walk Boston, which provides total joint replacement to financially vulnerable patients in the Dominican Republic, offers a unique opportunity to compare postoperative pain management approaches in a developed nation and a developing nation. METHODS: We interviewed American and Dominican surgeons and nurses (n = 22) during Operation Walk Boston 2015. We used a moderator's guide with open-ended questions to inquire about postoperative pain management and factors influencing prescribing practices. Interviews were recorded and transcripts were analyzed using content analysis. RESULTS: Providers highlighted differences in the patient-provider relationship, pain medication prescribing variability, and access to medications. Dominican surgeons emphasized adherence to standardized pain protocols and employed a paternalistic model of care, and American surgeons reported prescribing variability and described shared decision-making with patients. Dominican providers described limited availability of potent opioid preparations in the Dominican Republic, in contrast to American providers, who discussed opioid accessibility in the United States. CONCLUSIONS: Our findings suggest that cross-cultural comparisons provide insight into how opioid prescribing practices, approaches to the patient-provider relationship, and medication access inform distinct pain management strategies in American and Dominican surgical settings. Integrating lessons from cross-cultural pain management studies may yield more effective pain management strategies for surgical procedures performed in the United States and abroad.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".