“Playing in the Big Leagues Now”: Exploring Feedback Receptivity During the Transition to Residency
Bibliographic record
Abstract
Learners’ perceptions of feedback can significantly undermine its impact. Consequently, some feedback has been known to fall on deaf ears. At times when stress is heightened, however, feedback may hold value for both learning purposes and reassurance. Because stress and uncertainty are intensified during the steep transition from medical school to residency, we aimed to explore new residents’ receptivity to feedback and the characteristics of feedback that could optimise it at this stage in their training. Nine residents who were two to three months along in a residency program were recruited through voluntary sampling, then met individually for a semi-structured interview. Qualitative analysis of these interviews was conducted to explore new residents’ perception of their new context and their experiences with feedback, using a constructivist approach. Emerging themes and categories were developed inductively. Insights gained from our participants’ perspectives suggest that common circumstantial factors prompt novice residents to seek more guidance through feedback. In this study, novice residents were most receptive to feedback when its content was practical and aligned with residents’ personal objectives, when it was coherent with previous feedback and when it was discussed one-on-one in a setting which the resident considered safe. Participants expressed a need for more feedback on specific topics such as medical knowledge, clinical reasoning, prescribing, prioritizing, managing critically ill patients and dealing with increased anxiety. Medical teachers should be mindful of learners’ increased anxiety and uncertainty during the transition from medical school to postgraduate training, because more guidance may be needed during this period, including through feedback. Future research is needed to determine how this teaching momentum can best be utilized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".