Changes in performance: a 5‐year longitudinal study of participants in a multi‐source feedback programme
Bibliographic record
Abstract
OBJECTIVES: Multi-source feedback (MSF) enables performance data to be provided to doctors from patients, co-workers and medical colleagues. This study examined the evidence for the validity of MSF instruments for general practice, investigated changes in performance for doctors who participated twice, 5 years apart, and determined the association between change in performance and initial assessment and socio-demographic characteristics. METHODS: Data for 250 doctors included three datasets per doctor from, respectively, 25 patients, eight co-workers and eight medical colleagues, collected on two occasions. RESULTS: There was high internal consistency (alpha > 0.90) and adequate generalisability (Ep(2) > 0.70). D study results indicate adequate generalisability coefficients for groups of eight assessors (medical colleagues, co-workers) and 25 patient surveys. Confirmatory factor analyses provided evidence for the validity of factors that were theoretically expected, meaningful and cohesive. Comparative fit indices were 0.91 for medical colleague data, 0.87 for co-worker data and 0.81 for patient data. Paired t-test analysis showed significant change between the two assessments from medical colleagues and co-workers, but not between the two patient surveys. Multiple linear regressions explained 2.1% of the variance at time 2 for medical colleagues, 21.4% of the variance for co-workers and 16.35% of the variance for patient assessments, with professionalism a key variable in all regressions. CONCLUSIONS: There is evidence for the construct validity of the instruments and for their stability over time. Upward changes in performance will occur, although their effect size is likely to be small to moderate.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".