Physician performance feedback in a Canadian academic center
Bibliographic record
Abstract
Purpose This paper aims at the implementation and early evaluation of a comprehensive, formative annual physician performance feedback process in a large academic health-care organization. Design/methodology/approach A mixed methods approach was used to introduce a formative feedback process to provide physicians with comprehensive feedback on performance and to support professional development. This initiative responded to organization-wide engagement surveys through which physicians identified effective performance feedback as a priority. In 2013, physicians primarily affiliated with the organization participated in a performance feedback process, and physician satisfaction and participant perceptions were explored through participant survey responses and physician leader focus groups. Training was required for physician leaders prior to conducting performance feedback discussions. Findings This process was completed by 98 per cent of eligible physicians, and 30 per cent completed an evaluation survey. While physicians endorsed the concept of a formative feedback process, process improvement opportunities were identified. Qualitative analysis revealed the following process improvement themes: simplify the tool, ensure leaders follow process, eliminate redundancies in data collection (through academic or licensing requirements) and provide objective quality metrics. Following physician leader training on performance feedback, 98 per cent of leaders who completed an evaluation questionnaire agreed or strongly agreed that the performance feedback process was useful and that training objectives were met. Originality/value This paper introduces a physician performance feedback model, leadership training approach and first-year implementation outcomes. The results of this study will be useful to health administrators and physician leaders interested in implementing physician performance feedback or improving physician engagement.
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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 | 0.021 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".