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Record W4310639073 · doi:10.1177/23337214221138442

The Influence of Psychosocial Factors on Hospital Length of Stay Among Aging Canadians

2022· article· en· W4310639073 on OpenAlexafffundabout
Kelly Ann Renwick, Claudia Sanmartin, Kaberi Dasgupta, Lea Berrang‐Ford, Nancy A. Ross

Bibliographic record

VenueGerontology and Geriatric Medicine · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsStatistics CanadaQueen's UniversityMcGill University
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsPsychosocialSocial supportGerontologyMedicineCohortDemographyMultivariate analysisPsychologyPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Background: Hospital stays that are prolonged due to non-clinical factors are costly to health care systems and are likely suboptimal for patient well-being. We assessed the influence of psychosocial factors on hospital length of stay (LOS) for older Canadians in a retrospective cohort study. Data and Methods: Data from the Canadian Community Health Survey were linked with the Discharge Abstract Database. Analyses were stratified by age, 55–64 ( n = 1,060) and 65 and older ( n = 2,718). Main predictor variables of interest included four measures of social support, sense of belonging, and living alone. Multivariate models of LOS adjusted for age, sex, income, smoking, and frailty. Results: Among the younger respondents, low positive social interactions, low emotional/informational support, and living alone were associated with a longer LOS. Among respondents 65 and older, low affection, low positive social interactions, low emotional/informational support, and a weak sense of belonging were associated with a longer LOS. Discussion: Having low social support is associated with longer hospital stays in this Canadian cohort. Social support may influence LOS as risk factors for poor health and precarious care in the community. Mitigating these risk factors could reduce the economic burden that is played out through longer hospital stays.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.313
Teacher spread0.297 · 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 designObservational
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
Published2022
Admission routes3
Has abstractyes

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