Clinical Performance Feedback to Paramedics: What They Receive and What They Need
Bibliographic record
Abstract
OBJECTIVES: Performance feedback is not always well utilized in healthcare. To more effectively incorporate it, we used a discussion of current feedback systems to explore paramedics' perceived needs regarding feedback and to understand what feedback would improve their performance as healthcare providers. METHODS: We used a qualitative methodology with semistructured interviews of paramedics to explore perceptions and desires for feedback. Interpretive descriptive analysis was performed with continuous recruitment until thematic saturation was achieved. Themes were identified and a coding system was developed by two investigators separately and merged by consensus. The analysis was audited by a third investigator, and a member check was performed. RESULTS: Many different ideas were discussed that were analyzed to develop several major recurrent themes. One such theme was positive perception of feedback by paramedics. Despite the positive perceptions discussed, the shortcomings of current systems were also frequently discussed as were perceived barriers to receiving meaningful feedback. The idea of following up on patients' courses/outcomes also arose frequently during the interviews. In addition, feedback and its interaction with mental health emerged as a theme in terms of its potential for both positive and negative impact. Finally, suggestions about the future were also common with paramedics providing thoughts regarding what future systems could be developed or what changes could be made to provide them with meaningful feedback. CONCLUSIONS: Our findings show how paramedics perceive feedback, but still note how barriers may impair its uptake and how it may affect their mental health. Our participants also made recommendations about what they would want to see in future feedback systems. This information can provide the foundation to improve current feedback systems or structure new ones to allow paramedics to continue to develop themselves as healthcare professionals.
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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 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".