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Record W2017403857 · doi:10.12927/hcq.2007.18818

CIHI Survey: Hospital Readmissions for Patients with Mental Illness in Canada

2007· article· en· W2017403857 on OpenAlexaffabout
Nawaf Madi, Helen Hailin Zhao, Jerry Li

Bibliographic record

VenueHealthcare Quarterly · 2007
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsMental Health Research CanadaCanadian Institute for Health Information
Fundersnot available
KeywordsMental illnessMedicineRevolving doorMental healthPsychiatryHealth careAcute careFamily medicine

Abstract

fetched live from OpenAlex

In-patient hospital mental health services represent a fraction of the spectrum of services in place for those who seek treatment for a mental illness. It is often when the conditions of the illness become most severe that inpatient hospital services are sought. Hospitalization is an important means of stabilizing deteriorating psychiatric conditions, of re-establishing discontinued regimens of prescribed medication and of helping to transition individuals to outpatient and community-based services. Hospitalizations for mental illness impose a high cost in terms of healthcare expenditures (Jacobs et al. 2006) and a disruptive burden on the personal and professional lives of the individuals suffering from mental illness. Many such individuals experience a “revolving door” of multiple re-hospitalizations. This article provides information on the patterns of oneyear readmissions (for any reason) to acute care hospitals in Canada among patients with mental illness as the most responsible diagnosis in their index admission during 2002–2003. It is based on data from the Hospital Morbidity Database and Hospital Mental Health Database of the Canadian Institute for Health Information (CIHI). Readmissions were deemed if the individual had more than one episode of hospitalization during the period 2002–2003 to 2003–2004.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.015
GPT teacher head0.303
Teacher spread0.287 · 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

Citations45
Published2007
Admission routes2
Has abstractyes

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