A Way Forward in the COVID-19 Pandemic: Making the Case for Narrative Competence in Pulmonary and Critical Care Medicine
Bibliographic record
Abstract
Each surge of the coronavirus disease (COVID-19) pandemic presented new challenges to pulmonary and critical care practitioners. Although some of the initial challenges were somewhat less acute, clinicians now are left to face the physical, emotional, and mental toll of the past 2 years. The pandemic revealed a need for a more varied skillset, including space for reflection, tolerance of uncertainty, and humanism. These skills can assist clinicians who are left to heal from the difficulty of caring for patients in the absence of families who were excluded from the intensive care unit, public distrust of vaccines, and morgues overtaken by our patients. As pulmonary and critical care medicine practitioners and educators, we believe that cultivating practices, pedagogies, and institutional structures that foster narrative competence, "the ability to acknowledge, absorb, interpret, and act on the stories and plights of others," in our ourselves, our trainees, and our colleagues, may provide a productive way forward. In addition to fostering needed skills, this practice can promote necessary healing as well. This perspective introduces the practice of narrative competence, provides evidence of support for its implementation, and suggests opportunities for curricular integration.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.049 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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".