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Experiences of US Clinicians Contending With Health Care Resource Scarcity During the COVID-19 Pandemic, December 2020 to December 2021

2023· article· en· W4380871804 on OpenAlexaff
Catherine R. Butler, Aaron Wightman, Janelle S. Taylor, John L. Hick, Ann M. O’Hare

Bibliographic record

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversity of Toronto
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsThematic analysisPandemicQualitative researchHealth careMedicineCoronavirus disease 2019 (COVID-19)Theme (computing)Isolation (microbiology)PsychologyResource (disambiguation)Family medicineNursingPolitical scienceDiseaseSociology

Abstract

fetched live from OpenAlex

Importance: The second year of the COVID-19 pandemic saw periods of dire health care resource limitations in the US, sometimes prompting official declarations of crisis, but little is known about how these conditions were experienced by frontline clinicians. Objective: To describe the experiences of US clinicians practicing under conditions of extreme resource limitation during the second year of the pandemic. Design, Setting, and Participants: This qualitative inductive thematic analysis was based on interviews with physicians and nurses providing direct patient care at US health care institutions during the COVID-19 pandemic. Interviews were conducted between December 28, 2020, and December 9, 2021. Exposure: Crisis conditions as reflected by official state declarations and/or media reports. Main Outcomes and Measures: Clinicians' experiences as obtained through interviews. Results: Interviews with 23 clinicians (21 physicians and 2 nurses) who were practicing in California, Idaho, Minnesota, or Texas were included. Of the 23 total participants, 21 responded to a background survey to assess participant demographics; among these individuals, the mean (SD) age was 49 (7.3) years, 12 (57.1%) were men, and 18 (85.7%) self-identified as White. Three themes emerged in qualitative analysis. The first theme describes isolation. Clinicians had a limited view on what was happening outside their immediate practice setting and perceived a disconnect between official messaging about crisis conditions and their own experience. In the absence of overarching system-level support, responsibility for making challenging decisions about how to adapt practices and allocate resources often fell to frontline clinicians. The second theme describes in-the-moment decision-making. Formal crisis declarations did little to guide how resources were allocated in clinical practice. Clinicians adapted practice by drawing on their clinical judgment but described feeling ill equipped to handle some of the operationally and ethically complex situations that fell to them. The third theme describes waning motivation. As the pandemic persisted, the strong sense of mission, duty, and purpose that had fueled extraordinary efforts earlier in the pandemic was eroded by unsatisfying clinical roles, misalignment between clinicians' own values and institutional goals, more distant relationships with patients, and moral distress. Conclusions and Relevance: The findings of this qualitative study suggest that institutional plans to protect frontline clinicians from the responsibility for allocating scarce resources may be unworkable, especially in a state of chronic crisis. Efforts are needed to directly integrate frontline clinicians into institutional emergency responses and support them in ways that reflect the complex and dynamic realities of health care resource limitation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.020
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.026
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0120.009
Scholarly communication0.0060.004
Open science0.0010.007
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.116
GPT teacher head0.458
Teacher spread0.343 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2023
Admission routes1
Has abstractyes

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