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Record W1762793026 · doi:10.1300/j010v33n02_01

Predicting Length of Stay in an Acute Care Hospital

2001· article· en· W1762793026 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSocial Work in Health Care · 2001
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsMcGill University Health CentreMcGill UniversityMontreal General Hospital
Fundersnot available
KeywordsPsychosocialMedicineDemographicsProxy (statistics)Acute careMedical careEmergency medicinePsychiatryHealth careDemography

Abstract

fetched live from OpenAlex

In an acute care hospital, a major performance indicator is patient length of stay. This study, in a large university teaching acute care hospital in Canada, examined the effect of psychosocial problems on length of stay, controlling for patient demographics and medical condition. Average days stay for Diagnostic Related Groups (DRGs) was used as a proxy variable for severity of medical condition, and the Person-in-Environment (PIE) classification system was used to measure psychosocial problems. Data were collected on a sample of 160 patients; 78 in psychiatry and 82 in medical/surgical wards. In a regression analysis, the severity of the patient's psychosocial problem was a more significant predictor of length of stay than the DRG variable. The identification of psychosocial problems and their severity add an important and complementary dimension to research into the effectiveness of social workers in reducing length of stay. Workers found clients had significantly more problems related to their social role functioning than problems in the environment.

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.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.026
GPT teacher head0.412
Teacher spread0.386 · 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