Feedback to Supervisors: Is Anonymity Really So Important?
Bibliographic record
Abstract
PURPOSE: Research demonstrates that physicians benefit from regular feedback on their clinical supervision from their trainees. Several features of effective feedback are enabled by nonanonymous processes (i.e., open feedback). However, most resident-to-faculty feedback processes are anonymous given concerns of power differentials and possible reprisals. This exploratory study investigated resident experiences of giving faculty open feedback, advantages, and disadvantages. METHOD: Between January and August 2014, nine graduates of a Canadian Physiatry residency program that uses open resident-to-faculty feedback participated in semistructured interviews in which they described their experiences of this system. Three members of the research team analyzed transcripts for emergent themes using conventional content analysis. In June 2014, semistructured group interviews were held with six residents who were actively enrolled in the program as a member-checking activity. Themes were refined on the basis of these data. RESULTS: Advantages of the open feedback system included giving timely feedback that was acted upon (thus enhancing residents' educational experiences), and improved ability to receive feedback (thanks to observing modeled behavior). Although some disadvantages were noted, they were often speculative (e.g., "I think others might have felt …") and were described as outweighed by advantages. Participants emphasized the program's "feedback culture" as an open feedback enabler. CONCLUSIONS: The relationship between the feedback giver and recipient has been described as influencing the uptake of feedback. Findings suggest that nonanonymous practices can enable a positive relationship in resident-to-faculty feedback. The benefits of an open system for resident-to-faculty feedback can be reaped if a "feedback culture" exists.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".