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Record W1968097868 · doi:10.1177/0008417413506555

Exploring the occupations of homeless adults living with mental illnesses in Toronto / Explorer les occupations d’adultes sans-abri atteints de maladies mentales vivant à Toronto

2013· article· en· W1968097868 on OpenAlexaffvenueabout
Sarah C. Illman, Sandy Spence, Patricia O’Campo, Bonnie Kirsh

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2013
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsCentre for Global Health ResearchCoalition for Research in Women's HealthUniversity of TorontoWomen's College Hospital
Fundersnot available
KeywordsGerontologyMedicineMental healthPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The nature of occupational engagement for homeless people living with mental illnesses is not well understood, and there are few studies to date that examine the occupational lives of these individuals. PURPOSE: This research study seeks to understand how this group of individuals engages in occupations. The central question is "What is the nature of occupational engagement by homeless adults living with mental illnesses in Toronto?" METHOD: A constant comparative method of analysis was used in a secondary analysis of 60 interviews with homeless adults experiencing mental illness. FINDINGS: Four themes emerged that describe the nature of occupational engagement for this group: occupations as enjoyment, occupations as survival/risk, occupations as passing time, and occupations as self-management. Implications. This research informs occupational therapy interventions aimed at optimizing engagement, health, and well-being for homeless adults living with mental illnesses.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.996

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 studies0.0010.000
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.226
GPT teacher head0.434
Teacher spread0.208 · 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.

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

Citations29
Published2013
Admission routes3
Has abstractyes

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