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Record W4400808878 · doi:10.1155/2024/5935692

Home Staff Perspectives on Implementation of the Community Homes for Opportunity (CHO) Program in Southwestern Ontario

2024· article· en· W4400808878 on OpenAlexafffundabout
Cheryl Forchuk, Sebastian Gyamfi, Heba Hassan, Bryanna Lucyk, Richard Booth

Bibliographic record

VenueMental Illness · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsLawson Health Research InstituteUniversity of WindsorWestern University
FundersMinistry of Health, British Columbia
KeywordsModernization theoryMental healthFocus groupBaseline (sea)Independence (probability theory)NursingMedicinePsychologyGerontologyMedical educationPolitical scienceBusinessPsychiatryMarketing

Abstract

fetched live from OpenAlex

Introduction: Literature has established a bidirectional relationship between housing stability and mental health. Globally, there is a lack of affordable, safe, and appropriate housing for people with mental illness, and Canada is no exception. The current study explored the views of home staff on their experiences regarding the process of modernizing group homes (Community Homes for Opportunity [CHO]). It provided recommendations for further improvement of the implementation process. Method: We used ethnographic techniques to purposefully recruit 51 home staff from 28 group homes in Southwest Ontario, Canada. Focus group discussions were conducted at two‐time points (baseline: spring 2018 and postimplementation: winter 2019). Results: Data analysis produced four major themes. These include a general impression of the modernization process, facilitators, challenges to the implementation, and suggestions for improving the modernization program. Conclusion: Group homes such as CHO positively impact the well‐being and quality of life of persons with mental health and addiction problems while enhancing their independence and social integration for improved recovery outcomes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.892

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 communication0.0000.000
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 head0.084
GPT teacher head0.450
Teacher spread0.366 · 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 designQualitative
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

Citations1
Published2024
Admission routes3
Has abstractyes

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