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A Way Forward in the COVID-19 Pandemic: Making the Case for Narrative Competence in Pulmonary and Critical Care Medicine

2022· article· en· W4280591007 on OpenAlexaff
Rana Awdish, Margaret M. Hayes, Avraham Z. Cooper, Megan M. Hosey, Alison Trainor, Rosemary Weatherston, M. Elizabeth Wilcox

Bibliographic record

VenueATS Scholar · 2022
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsDistrustCompetence (human resources)NarrativePandemicCoronavirus disease 2019 (COVID-19)HumanismMedicinePsychologyNursingPublic relationsMedical educationPolitical sciencePsychotherapistSocial psychologyInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.090
GPT teacher head0.418
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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