1804 Feedback fatigue in the Foundation Year 1 Older Person's Unit cohort: A quality improvement project
Bibliographic record
Abstract
Abstract Introduction On designing and leading the Foundation Year 1 (FY1) Older Person’s Unit (OPU) teaching programme at St Thomas’ Hospital, London (STH), it was identified that the method of feedback collation was inefficient and yielding poor quality feedback from FY1s. Feedback fatigue was high. Plan FY1 trainees were initially asked to complete feedback for their FY1 OPU teaching on paper forms. This yielded a high response rate (100% of forms completed), but feedback quality was poor. The time taken to collate responses from the paper feedback forms was disproportionate to the quality of feedback received. Intervention 1 An online feedback form was designed and emailed to the FY1 trainees after each teaching session. This collated responses automatically into a password protected Excel spreadsheet. Study The online feedback form initially yielded a high response rate, along with constructive feedback. Time taken to collate responses was reduced to zero. However, was noted that the response rate fell gradually to approximately 20%. The two main factors inhibiting responses were a heavy email burden and forgetting to fill in the feedback form. Intervention 2 A QR code linked to the online feedback form was designed, with the intention of being shown at the end of each teaching session. This was emailed out to all presenters in advance and incorporated into their teaching presentations. Study Feedback response rate attained 100% consistently over a 2-month period. The feedback quality received was higher, with constructive comments being fed back in a timely matter. Conclusion Timely recognition of feedback fatigue in the FY1 trainee cohort is extremely important. Designing and implementing methods by which to negate and overcome this is important in obtaining feedback such that future teaching sessions can be continually improved and tailored to FY1 learning needs.
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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 | 0.026 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".