S’outiller pour mieux participer à la rétroaction : Un nouveau modèle cognitivo-comportemental destiné aux apprenants en médecine
Bibliographic record
Abstract
Sharing formative feedback is inherent in the supervision process and the acceptance of feedback by learners is an essential step in learning. However, receiving feedback from the supervisor evokes emotions and accepting it is not easy. Several recommendations guide preceptors on how to share feedback with learners and all emphasize the importance of encouraging the learner to actively interact in the feedback process. Although studies point to the positive effect of informing and training learners about feedback, few focus on their responsiveness to feedback. Under the rubric of developing a personal skill to better accept feedback, we propose a new behavioral model, called H.O.S.T., which aims to guide learners to approach feedback with a personal growth mindset associated with the learning position. Specifically, the model presents an interdependent set of attitudes and behaviors that aim to facilitate emotional management and engagement in the feedback process, in order to initiate the reflective process necessary for learning and to enable the acquisition of targeted skills. The acronym H.O.S.T. reminds students of the four essential elements of the behavioral model: humility, openness, shared explicitness and tenacity. Based on the positive psychology movement, each element is defined and justified by known theoretical concepts. In order to better assimilate the components of the model, the use of internal dialogue is adopted to facilitate the training and adoption of behaviors. The essence of the model is discussed in light of the feedback literacy dedicated to learners.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".