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Record W1903948227 · doi:10.3233/wor-2009-0886

Nonvocational outcomes of vocational rehabilitation: Reduction in health services utilization

2009· article· en· W1903948227 on OpenAlexaff
Yanina Jackson, Jill Kelland, Theodore D. Cosco, Diane C. McNeil, John R. Reddon

Bibliographic record

VenueWork · 2009
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsAlberta Hospital EdmontonAlberta Health Services
Fundersnot available
KeywordsVocational educationMental illnessRehabilitationAmbulatoryMental healthMedicineVocational rehabilitationAmbulatory careHealth careNursingPsychologyPsychiatryPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Numerous studies have demonstrated the beneficial effects of vocational rehabilitation on vocational outcomes in individuals with a mental illness, yet effects on secondary outcomes remain largely unexplored. This study investigates the impact of vocational rehabilitation on the utilization of emergency, ambulatory care and inpatient services in individuals with a mental illness. METHODS: Using a repeated measures study design, the utilization of health services by individuals with a mental illness (n= 37) was compared before and during their engagement in training and employment at a social enterprise - a form of vocational rehabilitation. RESULTS: Individuals with a mental illness had significantly less emergency department visits (p=0.01), ambulatory care visits (p=0.01) and hospital admissions (p=0.05), but no difference in hospital length of stay (p=0.39), during training/employment, compared to pre-training/employment at a social enterprise. CONCLUSION: The reduction in health services utilization found in this study may reflect symptom and overall health improvement, highlighting the importance of vocational rehabilitation programs for individuals with a mental illness. In addition, results from this study can inform stakeholders and policymakers about the impact of vocational rehabilitation on the healthcare system to help guide decisions regarding program implementation or continuation, and funding allocation.

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 score0.198
Threshold uncertainty score0.192

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 head0.027
GPT teacher head0.357
Teacher spread0.330 · 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

Citations16
Published2009
Admission routes1
Has abstractyes

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