Pain management among Dominican patients with advanced osteoarthritis: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Advanced osteoarthritis and total joint replacement (TJR) recovery are painful experiences and often prompt opioid use in developed countries. Physicians participating in the philanthropic medical mission Operation Walk Boston (OpWalk) to the Dominican Republic have observed that Dominican patients require substantially less opioid medication following TJR than US patients. We conducted a qualitative study to investigate approaches to pain management and expectations for postoperative recovery in patients with advanced arthritis undergoing TJR in the Dominican Republic. METHODS: We interviewed 20 patients before TJR about their pain coping mechanisms and expectations for postoperative pain management and recovery. Interviews were conducted in Spanish, translated, and analyzed in English using content analysis. RESULTS: Patients reported modest use of pain medications and limited knowledge of opioids, and many relied on non-pharmacologic therapies and family support to cope with pain. They held strong religious beliefs that offered them strength to cope with chronic arthritis pain and prepare for acute pain following surgery. Patients exhibited a great deal of trust in powerful others, expecting God and doctors to cure their pain through surgery. CONCLUSION: We note the importance of understanding a patient's individual pain coping mechanisms and identifying strategies to support these coping behaviors in pain management. Such an approach has the potential to reduce the burden of chronic arthritis pain while limiting reliance on opioids, particularly for patients who do not traditionally utilize powerful analgesics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".