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Looking to “Level the Field”: A Qualitative Study of How Clinicians Operationalize Social Determinants in Critical Care

2024· article· en· W4401353727 on OpenAlexaff
Deepa Ramadurai, Heta Patel, Jacqueline Chan, Juliet Young, Justin T. Clapp, Joanna L. Hart

Bibliographic record

VenueAnnals of the American Thoracic Society · 2024
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsInstitute of Health Economics
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineOperationalizationQualitative researchField (mathematics)MEDLINESocial scienceEpistemology

Abstract

fetched live from OpenAlex

Abstract Rationale Current critical care practice does not integrate social determinants of health (SDOH) in systematic or standardized ways. Routine assessment of SDOH in the intensive care unit (ICU) may improve clinical decision making, patient- and family-centered outcomes, and clinician well-being. Objective Given that the appropriateness and feasibility of SDOH assessment in the ICU is unknown, we aimed to understand how ICU clinicians think about and use SDOH. Methods We conducted semistructured interviews with clinicians focused on barriers to and facilitators of assessing SDOH during critical illness and perceptions of screening for SDOH in the ICU. We used chart-stimulated recall to assist clinicians in reflecting on how SDOH applied to and was used in patients’ care. After deidentifying interviews, we analyzed transcripts guided by a thematic analysis approach using a combination of inductive and deductive coding, the latter framed within the Centers for Disease Control and Prevention SDOH Healthy People framework. Results We completed interviews with 30 clinicians in a variety of professional roles. The majority of clinicians self-identified as men (n = 17; 56.7%) of White race (n = 25; 83.3%). Clinicians contextualize their use of SDOH within three frames of reference: 1) their own identity and experiences; 2) their relationships and communication with patients and caregivers; and 3) immediate structures of care around ICU patients, including clinician advocacy, care transitions, and readmission. Clinicians identified that discussing SDOH could allow them to recognize bias faced by their patients, elucidate drivers of critical illness, and navigate communication with patients’ caregivers. Clinicians worried about ICU-specific factors impeding the discussion of SDOH, including time constraints and acuity, high stakes and emotions, and negative anticipatory emotions. Conclusions Clinicians gather SDOH during critical illness both to understand their patients’ stories and to provide individualized care, which may lead to better clinician satisfaction and patient- and family-centered care outcomes. Educational and operational efforts to increase SDOH assessment and use in critical care should also gather and integrate the perspectives of patients and caregivers regarding the collection and use of SDOH in the ICU.

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)0.041
metaresearch head score (Gemma)0.059
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 score0.041
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.059
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0170.024
Scholarly communication0.0070.009
Open science0.0030.010
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.613
GPT teacher head0.686
Teacher spread0.073 · 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".

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Citations1
Published2024
Admission routes1
Has abstractyes

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