Bibliographic record
Abstract
PURPOSE: To evaluate a new assessment tool measuring physicians' academic productivity and its use in a performance-based remuneration system. METHOD: The authors developed an assessment tool based on existing tools to measure productivity. Yearly, from 2008 to 2011, physicians at the University of Western Ontario received a score of up to three points for each of four components (impact, application, scholarly activity, mentorship) in each of four domains (clinical practice, education, research, administration). Scores were weighted by the percentage of time physicians spent on tasks in each domain. Year 1 scores were a baseline. In Years 2 and 3, scores were tied to remuneration. The authors compared scores and associations, accounting for age and academic rank, across the three years. RESULTS: The 37 participating physicians included 11 assistant, 23 associate, and 4 full professors. The mean weighted total baseline score across all four domains was 7.44. Years 2 and 3 scores were highly correlated with Year 1 scores (r = 0.85, Years 1 and 2; r = 0.89, Years 1 and 3). Year 2 mean weighted scores did not differ significantly from Year 1 scores. Assistant professors' scores improved significantly between Years 1 and 2 (+1.08, P < .001). Lower Year 1 scores were correlated with a greater improvement in scores between Years 1 and 2, and age was negatively correlated with score changes between Years 2 and 3. CONCLUSIONS: Although the tool may be a robust measurement of physicians' productivity, performance-based remuneration had no effect on physicians' overall performance.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".