Effects Of Knowing The Patient'e Life Story On The Quality Of The Doctor-Patient Relationship In Primary Care
Bibliographic record
Abstract
Background: We wondered if primary care physicians' knowing their patients' life stories would improve the quality of the doctor-patient relationship for both parties and if this would have an impact upon chronic pain, anxiety, or depression.Methods: Doctors and patients completed the Doctor-Patient Quality of Relationship Questionaire - 16. Patients also completed the McGill Pain Inventory, the Zung Anxiety Inventory, and the Center for Epidemiological Studies Depression Scale. Then patient life stories were obtained using the Northwestern University Life Story Interview. The stories were read by the patients' physicians and placed in the electronic health record. Questionnaires were repeated at intervals of 4, 8, and 12 months.Results: The quality of the doctor-patient relationship improved statistically significantly for both parties (from 3.8 to 4.3 for patients, p = 0.0316; from 3.43 to 4.11 for physicians, p = 0.0042. Ratings of pain on the McGill Pain Inventory also improved statistically significantly, but not ratings of anxiety and depression.Conclusions: Obtaining a life story from a patient with chronic pain is an effective intervention in primary care with effect sizes in line with other more standard chronic pain treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.028 |
| 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) | 0.007 | 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".