Peer-assisted debriefing of multisource feedback: an exploratory qualitative study
Bibliographic record
Abstract
BACKGROUND: The Manitoba Physician Achievement Review (MPAR) is a 360-degree feedback assessment that physicians undergo every 7 years to retain licensure. Deliberate reflection on feedback has been demonstrated to encourage practice change. The MPAR Reflection Exercise (RE), a peer-assisted debriefing tool, was developed whereby the physician selects a peer with whom to review and reflect on feedback, committing to change. This qualitative study explores how physicians who had undergone the MPAR used the RE, what areas of change are identified and committed to, and what they perceived as the role of reflection in the MPAR process. METHODS: The MPAR RE was piloted out to a cohort of MPAR-reviewed physicians. Thematic analysis was conducted on completed exercises (n = 61). Semi-structured interviews were conducted with individuals (n = 6) who completed the MPAR RE until saturation was reached. RESULTS: Physicians reviewed feedback with a range of peers, including colleagues, staff, and spouses. Many physicians were surprised by feedback, both positive and negative, but interviewees found the RE useful in processing feedback. Areas where physicians committed to change were diverse, covering all CanMEDS roles. Most physicians identified themselves as being successful in implementing change, though time, habit, and structures were cited as barriers. CONCLUSIONS: Peer-assisted debriefing can assist reflection of multisource feedback. It is easy to implement, is not resource-intensive, and feedback implies that it is effective at promoting change. Participants, with the aid of peers, identified areas for change, developed approaches for change, and largely thought themselves successful at implementing changes. Areas of change included all seven CanMEDS roles.
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".