The relative contribution of patient, provider and organizational influences to the appropriate diagnosis and management of diabetes mellitus
Bibliographic record
Abstract
OBJECTIVE: To estimate the relative contribution of patient attributes, provider characteristics and organizational features of the doctors' workplace to the diagnosis and management of diabetes. RESEARCH DESIGN AND METHODS: In a factorial experimental design doctors (n = 192) viewed clinically authentic vignettes of 'patients' presenting with identical signs and symptoms. Doctor subjects were primary care doctors stratified according to gender and level of experience. During an in-person interview scheduled between real patients, doctors were asked how they would diagnosis and manage the vignette 'patients' in clinical practice. RESULTS: This study considered the relative contribution of patient, doctor and organizational factors. Taken together patient attributes explained only 4.4% of the variability in diabetes diagnosis. Doctor factors explained only 2.0%. The vast majority of the explained variance in diabetes diagnosis was due to organizational factors (14.3%). Relative contributions combined (patient, provider, organizational factors) explained only 20% of the total variance. CONCLUSION: Attempts to reduce health care variations usually focus on the education/activation of patients, or increased training of doctors. Our findings suggest that shifting quality improvement efforts to the area which contributes most to the creation and amplification of variations (organizational influences) may produce better results in terms of reduced variations in health care associated with diabetes.
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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 | 0.008 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".