Using literature to promote reflection in medical school
Bibliographic record
Abstract
Background:In the last half century, literature has been increasingly recognized as beneficial to medical trainees in terms of teaching empathy, cultural competence, listening and interpretation skills, and reflecting on moral issues (Jones, A. H., 2013). Following the success of a McGill medical students reading club, a reading module was tentatively added to the McGill Physician Apprenticeship (PA) Course curriculum in 2016. This was based on the idea that literature could be used a springboard towards discussion and reflection upon issues related to health, illness and medical care. Methodology:PA groups are composed of six medical students, two senior students and a physician mentor, whose regular meetings throughout medical school are structured around suggested modules. As part of the reading module, all PA groups were encouraged to choose a novel, essay, short story or poem by the end of their academic first year. A discussion on the reading was to be held in early second year, with specific questions to be addressed in order to reflect on course objectives (e.g., patient-centered clinical approach, reflective practice, healer role). The students anonymously completed pre and post-module questionnaires to assess the module's learning and reflective value. Students’ opinions on the pertinence of renewing this pilot project in the future were also accumulated. Results:The first student cohort has completed the pre-questionnaires in spring 2017 and will be completing the post-module questionnaires in fall 2017.Therefore, at the moment of the abstract submission, we do not have preliminary results.
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 | 0.048 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".