Bibliographic record
Abstract
This paper draws on the results of an eight‐month research project designed to foster reflection and professional growth in an intergenerational group of medical students, residents, and newly practicing physicians. Over a period of several months, participants individually read books written by physician authors of their experiences in clinical practice. After each book reading, the group met and engaged in a tape‐recorded and researcher‐facilitated discussions about the book. Reading followed by discussion was chosen as a process to promote increased self‐reflection amongst the participants. Participants reflected on the readings and on their own experiences of moving through professional education in the process of becoming physicians. There were eight meetings. A thematic analysis of the eight transcripts of group meetings identified twelve overarching themes. Nine are discussed in this paper. The three additional themes address the participants’ experiences of the process and are described elsewhere. The nine themes range from the ‘image of super doc’ to ‘learning from patients’. Several themes contained sub‐themes. The themes resonate with other findings in medical education about the experience of becoming physicians. The themes are discussed in relation to other research on designing spaces for reflective growth in medical education. Insights into the benefits of an intergenerational group experience are discussed.
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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".