Strengthening the feedback culture in a postgraduate residency program
Bibliographic record
Abstract
Background: Feedback is defined as specific information presented to a learner that facilitates professional development through the process of reflection. Timely provision of constructive feedback to learner is important in optimizing the learning curve. The aim of the current study was to see the effectiveness of various interventions on feedback practices of faculty members. Methods: This is a quasi-experimental study (pre- and postdesign). It was conducted from November 2009 to March 2011 at The Aga Khan University, Pakistan. Faculty development workshops, allotment of specified feedback time, and restructuring of residency feedback forms were done as interventions. Data collection was done pre- and postintervention. Resident's and faculty satisfaction regarding the feedback process were evaluated using a prepiloted questionnaire. Paired t-test was applied to assess the effect of interventions on faculty and resident's satisfaction. Results: The mean satisfaction scores of residents were significantly improved (P < 0.05). Pre- and postintervention faculty satisfaction score also demonstrated significant difference in overall satisfaction level, from 47.88 ± 13.92 to 63.40 ± 8.72 (P < 0.05). Discussion: This study showed improved faculty engagement and satisfaction for the provision of feedback to the trainee resident. Strengthening this, culture requires continuous reinforcement, individualized feedback to the faculty members regarding their feedback practices, and continuing faculty development initiatives.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".