Bibliographic record
Abstract
We thank Ms. Shemtob for her thoughtful response to our article. She makes the important point that reflection as a tool is an unfamiliar concept and one that does not come naturally. We would add that this is especially true in the context of medical education with its well-known associated pressures and situating one’s learning within a vast and ever-increasing body of knowledge. While Ms. Shemtob suggests that students must develop their own styles of reflection and opportunities to reflect, we want to propose that medical education can and must create opportunities for shared reflective practice and perhaps even methods for reflective practice that resonate with the context and content of clinical practice. This position takes the view that reflective practice does not need to consist of exclusively solitary and self-directed thought but can also include collective dialogue and interactions with others. If Ms. Shemtob is suggesting that the “independent end” for reflection is personal development or improvement of practice, we would add that it could potentially encompass much more. Flexibility, as she indicates, is certainly the key. Perhaps, as many students believe, reflection or reflective practice cannot be taught; however, a caveat would be that various methods for reflective practice can be taught. While many programs currently encourage reflection through reflective writing, there are nevertheless countless other means to facilitate reflective practice, including journals, portfolios, movie reviews, and even art, some of which Ms. Shemtob alludes to in her own practice. Notably, a global context for reflective practice in medical education also takes on some significance for medical educators who seek to transfer skills and practices to international settings, especially in countries that have histories of colonization or deference to ways of knowing from the developed world. Here medical educators must also engage actively in critical reflection to respond respectfully and effectively to the contextual needs of medical education outside of Western industrial societies. The concepts of cultural humility and contextual reflexivity can serve as orientating mechanisms to guide the process. Thirusha Naidu, PhDClinical psychologist, Department of Behavioural Medicine, University of KwaZulu-Natal, Durban, South Africa; [email protected] Arno Kumagai, MDVice chair for education, Department of Medicine, University of Toronto, Toronto, Ontario, Canada.
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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 | 0.006 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.029 | 0.054 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".