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Record W3183328395 · doi:10.12927/hcq.2021.26549

Do Not Waste a Crisis: Physician Engagement during the COVID-19 Pandemic

2021· article· en· W3183328395 on OpenAlexaffvenue
Meiqi Guo, Richard Dunbar‐Yaffe, Erin Bearss, Sabrina Lim-Reinders, Christine Soong

Bibliographic record

VenueHealthcare Quarterly · 2021
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsToronto Rehabilitation InstituteSinai Health SystemUniversity Health Network
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Public engagementNursingPsychologyMedicinePublic relationsPolitical scienceInfectious disease (medical specialty)Virology

Abstract

fetched live from OpenAlex

Physician engagement is an important factor in improving care quality and patient safety, but engaging physicians is not easy. Winston Churchill's famous assertion about never wasting a crisis has defined the approach taken by many leaders during the COVID-19 pandemic. This paper describes three case studies of successful physician engagement across the continuum of acute care, chronic care and primary care settings during the pandemic. These examples offer insights on physician engagement within unique settings by leveraging intrinsic motivators and Spurgeon's model of medical engagement.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.127
GPT teacher head0.440
Teacher spread0.313 · 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.

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

Citations2
Published2021
Admission routes2
Has abstractyes

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