The Hidden Influences: Psychological Drivers of Medical Practice Variation
Bibliographic record
Abstract
Background: Previous research showed that the majority of the variation in providers’ practice patterns is unexplained by patient, physician, and primary care practice characteristics. This study assessed physicians’ personal behavioral characteristics as explanatory components of medical practice variation (MPV). Methods: In this cross-sectional study, primary care physicians from Clalit Health Services in southern Israel were interviewed using validated surveys assessing risk-taking, tolerance for ambiguity, stress due to uncertainty, fear of malpractice, and empathy. We analyzed how much these traits explained MPV compared to patient, physician demographic, occupational, and practice characteristics using generalized linear mixed models and Nakagawa’s R2. Results: Of the 160 physicians approached, 146 (91.3%) participated. The median practicing time was 22 years; 48% were male, with a median age of 49. The median number of patients per practice was 1135. Overall, 40.4% of MPV was explained, mostly by patient characteristics (18.9%), practice characteristics (10.2%), and physician demographics (8.3%). Physician behavioral traits explained only 2.3%. Conclusions: Personal behavior characteristics explain a minority of MPV, leaving 60% of the MPV unexplained. This suggests either limitations in survey assessments or that these traits are not key drivers of MPV.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".