Who am I? Narratives as a window to transformative moments in critical care
Bibliographic record
Abstract
Critical care clinicians practice a liminal medicine at the border between life and death, witnessing suffering and tragedy which cannot fail to impact the clinicians themselves. Clinicians' professional identity is predicated upon their iterative efforts to articulate and contextualize these experiences, while a failure to do so may lead to burnout. This journey of self-discovery is illuminated by clinician narratives which capture key moments in building their professional identity. We analyzed a collection of narratives by critical care clinicians to determine which experiences most profoundly impacted their professional identity formation. After surveying 30 critical care journals, we identified one journal that published 84 clinician narratives since 2013; these constituted our data source. A clinician educator, an art historian, and an anthropologist analyzed these pieces using a narrative analysis technique identifying major themes and subthemes. Once the research team agreed on a thematic structure, a clinician-ethicist and a trainee read all the pieces for analytic validation. The main theme that emerged across all these pieces was the experience of existing at the heart of the dynamic tension between life and death. We identified three further sub-themes: the experience of bridging the existential divide between dissimilar worlds and contexts, fulfilling divergent roles, and the concurrent experience of feeling dissonant emotions. Our study constitutes a novel exploration of transformative clinical experiences within Critical Care, introducing a methodology that equips medical educators in Critical Care and beyond to better understand and support clinicians in their professional identity formation. As clinician burnout soars amidst increasing stressors on our healthcare systems, a healthy professional identity formation is an invaluable asset for personal growth and moral resilience. Our study paves the way for post-graduate and continuing education interventions that foster mindful personal growth within the medical subspecialties.
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.022 | 0.041 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".