Pharmacological Prescribing and Satisfaction with Pain Treatment Among Non-Hispanic Black Men with Chronic Pain
Bibliographic record
Abstract
Introduction: Pharmacological strategies are often central to chronic pain management; however, pain treatment among non-Hispanic Black men may differ because of their disease profiles and healthcare interactions. However, less is known about pain medication prescribing and patients' satisfaction with pain treatment and management among non-Hispanic Black men with self-reported chronic pain. Purpose: This study assessed factors associated with non-Hispanic Black men being prescribed/recommended narcotics/opioids for chronic pain and their satisfaction with pain treatment/management. Methods: Data were analyzed from 286 non-Hispanic Black men with chronic pain who completed an internet-delivered questionnaire. Participants were recruited nationwide using a Qualtrics web-based panel. Logistic regression was used to identify factors associated with being prescribed/recommended narcotics/opioids for pain management treatment. Then, ordinary least squares regression was used to identify factors associated with their satisfaction level with the pain treatment/management received. Results: On average, participants were 56.2 years old and 48.3% were prescribed/recommended narcotics/opioids for chronic pain. Men with more chronic conditions (Odds Ratio [OR] = 0.57, P = 0.043) and depression/anxiety disorders (OR = 0.53, P = 0.029) were less likely to be prescribed/recommended narcotics/opioids. Men who were more educated (OR = 2.09, P = 0.044), reported more frequent chronic pain (OR = 1.28, P = 0.007), and were allowed to participate more in decisions about their pain treatment/management (OR = 1.11, P = 0.029) were more likely to be prescribed/recommended narcotics/opioids. On average, men with more frequent chronic pain (B = -0.25, P = 0.015) and pain problems (B = -0.16, P = 0.009) were less satisfied with their pain treatment/management. Men who were allowed to participate more in decisions about their pain treatment/management reported higher satisfaction with their pain treatment/management (B = 0.55, P < 0.001). Conclusion: Playing an active role in pain management can improve non-Hispanic Black men's satisfaction with pain treatment/management. This study illustrates the importance of patient-centered approaches and inclusive patient-provider interactions to improve chronic pain management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".