“You’re Not Trying to Save Somebody From Death”: Learning as “Becoming” in Palliative Care
Bibliographic record
Abstract
PURPOSE: Learning can be conceptualized as a process of "becoming," considering individuals, workplace participation, and professional identity formation. How postgraduate trainees learn palliative care, encompassing technical competence, compassion, and empathy, is not well understood or explained by common conceptualizations of learning as "acquisition" and "participation." Learning palliative care, a practice that has been described as a cultural shift in medicine challenging the traditional role of curing and healing, provided the context to explore learning as "becoming." METHOD: The authors undertook a qualitative narrative study, interviewing 14 residents from the University of Ottawa Family Medicine Residency Program eliciting narratives of memorable learning (NMLs) for palliative care. Forty-five NMLs were analyzed thematically. To illuminate the interplay among themes, an in-depth analysis of the NMLs was done that considered themes and linguistic and paralinguistic features of the narratives. RESULTS: Forty-five NMLs were analyzed. The context of NMLs was predominantly a variety of clinical workplaces during postgraduate training. Themes clustered around the concept of palliative care and how it contrasted with other clinical experiences, the emotional impact on narrators, and how learning happened in the workplace. Participants had expectations about their identities as doctors that were challenged within their NMLs for palliative care. CONCLUSIONS: NMLs for palliative care were a complex entanglement of individual experience and social and workplace cultures highlighting the limitations of the "acquisition" and "participation" metaphors of learning. By conceptualizing learning as "becoming," what occurs during memorable learning can be made accessible to those supporting learners and their professional identity formation.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".