Experiences of Faculty Members Giving Corrective Feedback to Medical Trainees in a Clinical Setting
Bibliographic record
Abstract
INTRODUCTION: Imperative to medical training is the observation and provision of feedback. In this era of competency-based medical education, feedback is one of the core components of this new model. A better understanding of the medical faculty's attitudes and experiences when providing feedback is essential. Currently, there are limited qualitative studies that have explored attitudes and experiences of faculty members when giving corrective feedback to medical trainees. METHODS: To allow an in-depth exploration of this phenomenon, a hermeneutics phenomenology approach was used, by conducting semistructured interviews with 10 faculty members representing six disciplines and used thematic analysis to create data-driven codes and identify key themes through an iterative consensus-building process. RESULTS: Four themes were identified by the authors: (1) Elements of effective feedback, (2) Faculty members' perception of giving corrective feedback, (3) Challenges as it relates to the assessment culture of the institution, and (4) Providing effective corrective feedback as a mutual process focused on relationship building between learners and preceptors. DISCUSSION: By exploring faculty members' perceptions of providing perceived corrective feedback, we identified actionable recommendations based on the study participants' experiences, expectations, and challenges which could be addressed involving future faculty development with the focus on modifying concepts of feedback and institutional changes that will promote an attitudinal and a cultural shift.
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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 | 0.020 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".