Performance feedback: An exploratory study to examine the acceptability and impact for interdisciplinary primary care teams
Bibliographic record
Abstract
BACKGROUND: This mixed methods study was designed to explore the acceptability and impact of feedback of team performance data to primary care interdisciplinary teams. METHODS: Seven interdisciplinary teams were offered a one-hour, facilitated performance feedback session presenting data from a comprehensive, previously-conducted evaluation, selecting highlights such as performance on chronic disease management, access, patient satisfaction and team function. RESULTS: Several recurrent themes emerged from participants' surveys and two rounds of interviews within three months of the feedback session. Team performance measurement and feedback was welcomed across teams and disciplines. This feedback could build the team, the culture, and the capacity for quality improvement. However, existing performance indicators do not equally reflect the role of different disciplines within an interdisciplinary team. Finally, the effect of team performance feedback on intentions to improve performance was hindered by a poor understanding of how the team could use the data. CONCLUSIONS: The findings further our understanding of how performance feedback may engage interdisciplinary team members in improving the quality of primary care and the unique challenges specific to these settings. There is a need to develop a shared sense of responsibility and agenda for quality improvement. Therefore, more efforts to develop flexible and interactive performance-reporting structures (that better reflect contributions from all team members) in which teams could specify the information and audience may assist in promoting quality improvement.
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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 | 0.057 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".