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Record W2992635622 · doi:10.1007/s11414-019-09685-1

Frequent Attendance to the Emergency Department after Release from Prison: a Prospective Data Linkage Study

2019· article· en· W2992635622 on OpenAlexafffund
Amanda Butler, Alexander Love, Jesse T Young, Stuart A. Kinner

Bibliographic record

VenueThe Journal of Behavioral Health Services & Research · 2019
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsSimon Fraser University
FundersNational Health and Medical Research CouncilCanadian Institutes of Health ResearchDepartment of Education and TrainingUniversity of MelbourneAustralian GovernmentUniversity of QueenslandQueensland Health
KeywordsEmergency departmentAttendanceLogistic regressionMedicinePrisonMental illnessPopulationDemographyMental healthLongitudinal studyPsychiatryFamily medicinePsychologyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

The aim of this paper was to identify characteristics and predictors of frequent emergency department (ED) use among people released from prisons in Queensland, Australia. Baseline interview data from a sample of sentenced adults were linked to ED and hospital records. The association between baseline characteristics and frequent ED attendance was modelled by fitting multivariate logistic regression models. Participants who had ≥ 4 visits to the ED in any 365-day period of community follow-up were defined as frequent attenders (FA). The analyses included 1307 people and mean follow-up time in the community was 1063 days. After adjusting for covariates, those with a dual diagnoses of mental illness and substance use (RR = 2.42, 95% CI 1.47-3.99) and those with mental illness alone (RR = 2.47, 95% CI 1.29-4.73) were at higher risk of frequent ED attendance, compared with those with no disorder. Future research should assess whether individually tailored transition supports from prison to community reduce the frequency of ED use among this population.

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)0.004
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 score0.034
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.083
GPT teacher head0.468
Teacher spread0.384 · 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

Citations27
Published2019
Admission routes2
Has abstractyes

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