Feedback on clinical team performance: how does it work, in what contexts, for whom, and for what changes? A critical realist qualitative multiple case study
Bibliographic record
Abstract
BACKGROUND: Feedback on clinical performance aims to provide teams in health care settings with structured results about their performance in order to improve these results. Two systematic reviews that included 147 randomized studies showed unresolved variability in professional compliance with desired clinical practices. Conventional recommendations for improving feedback on clinical team performance generally appear decontextualized and, in this regard, idealized. Feedback involves a complex and varied arrangement of human and non-human entities and interrelationships. To explore this complexity and improve feedback, we sought to explain how feedback on clinical team performance works, for whom, in what contexts, and for what changes. Our goal in this research was to present a realistic and contextualized explanation of feedback and its outcomes for clinical teams in health care settings. METHODS: This critical realist qualitative multiple case study included three heterogeneous cases and 98 professionals from a university-affiliated tertiary care hospital. Five data collection methods were used: participant observation, document retrieval, focus groups, semi-structured interviews, and questionnaires. Intra- and inter-case analysis performed during data collection involved thematic analysis, analytical questioning, and systemic modeling. These approaches were supported by critical reflexive dialogue among the research team, collaborators, and an expert panel. RESULTS: Despite the use of a single implementation model throughout the institution, results differed on contextual decision-making structures, responses to controversy, feedback loop practices, and use of varied technical or hybrid intermediaries. Structures and actions maintain or transform interrelationships and generate changes that are in line with expectations or the emergence of original solutions. Changes are related to the implementation of institutional and local projects or indicator results. However, they do not necessarily reflect a change in clinical practice or patient outcomes. CONCLUSIONS: This critical realist qualitative multiple case study offers an in-depth explanation of feedback on clinical team performance as a complex and open-ended sociotechnical system in constant transformation. In doing so, it identifies reflexive questions that are levers for the improvement of team feedback.
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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 | 0.053 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| 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".