Patient pain: its influence on primary care physician-patient interaction.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Heightened awareness of the importance of appropriate pain management in health care delivery has stimulated researchers to examine the impact of patient pain on medical encounters. In this study, we explored how patient pain might influence the physician-patient interaction during medical visits. METHODS: New adult patients (n = 509) were randomized to see primary care physicians in videotaped visits at a university medical center Self-reported patient pain was measured before the visit using the Visual Analog Scale and the Medical Outcomes Study Short Form-36 (MOS SF-36) pain scale; patient sociodemographics were also measured. Physician practice style during the visit was analyzed with the Davis Observation Code (DOC). RESULTS: Regression analyses revealed that patient pain during the medical visit was associated with the physician spending a greater portion of the visit on technical tasks and a smaller portion on preventive services and other activities designed to encourage the patients' active participation in their own health care. CONCLUSIONS: Patient pain may influence the physician-patient interaction and its outcomes. Primary care physicians should be aware that there may be less focus on patients' active involvement in their own care and less emphasis on providing disease prevention when treating patients who are experiencing pain.
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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 | 0.012 |
| 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 | 0.001 |
| 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".