Effects of a Life Story Interview on the Physician–Patient Relationship with Chronic Pain Patients in a Primary Care Setting
Bibliographic record
Abstract
Introduction: Within family medicine it is generally accepted that the more we know about patients' lives, the better the care we provide. Few studies have sought to quantify this historical assumption. We wondered if knowing their chronic pain, patients' life stories would improve the physician–patient relationship in a family medicine residency training program clinic. Methods: We selected patients in chronic pain with depression and/or anxiety who were considered difficult. After a lead in period to establish stability of ratings, we obtained a life story interview for 125 such patients after administering the doctor–patient relationship questionnaire to them and their physicians. Patients completed the McGill Pain Inventory (MPQ), the Zung Anxiety Inventory, and the Center for Epidemiological Studies Depression Scale. Physicians completed the Jefferson Physicians Empathy Scale. Questionnaires were repeated every 4 months. Results: The quality of the physician–patient relationship increased significantly over the course of the year for patients (increase of 0.60, standard deviation [SD] = 0.13, 95% confidence interval [CI] = 0.57 to 0.63, p < 0.001) and for doctors (increase of 0.77, SD = 0.20, 95% CI = 0.72 to 0.81, p < 0.001). The perceived level of pain on the MPQ decreased significantly on the sensory component (71.2 ± 7.6 to 11.7 + 9.4, 95% CI = 0.589 to 9.411, p = 0.0270 and the affective component (4.2 + 3.4 to 2.1 + 4.3, 95% CI = 0.131 to 4.069, p = 0.037). Anxiety and depression ratings did not change. Physicians' empathy ratings increased significantly over the course of the year from a mean of 117.2 (SD = 10.2) to 125.1 (SD = 16.1) for a difference of 7.90, which was significant at p = 0.0273 with a 95% CI of −14.85 to −0.915. Discussion: Knowing the patient's life story improves the physician–patient relationship for both patients and physicians. When the physician–patient relationship improves, the perceived level of pain decreases. Physicians' empathy ratings increase. While the interview requires 90–120 min, it is billable, and can be done by medical students, medical assistants, social workers, or behavioral health. Conclusions: Obtaining life stories of chronic pain patients is a cost-effective way to reduce pain while simultaneously improving the physician–patient relationship and increasing physician empathy.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".