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Record W2327120455 · doi:10.7870/cjcmh-2009-0011

“Reengineering” Residential Care Facilities: A Case Study of Hamilton, Ontario

2009· article· en· W2327120455 on OpenAlexaffvenueabout
Sara Edge, Robert Wilton

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2009
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBusiness process reengineeringResidential careAssisted Living FacilityBusinessMental healthRehabilitationQuality (philosophy)Assisted livingPsychologyGerontologyEnvironmental healthMedicineMarketingPsychiatry

Abstract

fetched live from OpenAlex

Residential care facilities are an enduring feature of Ontario's community mental health system. While these facilities offer affordable housing, they are also custodial environments that are poorly suited to goals of recovery and rehabilitation. This paper examines the extent to which facilities can be “reengineered” to improve tenants' daily lives using a case study of facilities in Hamilton, Ontario. Data from a 2005 survey of 50 people with psychiatric disabilities living in residential care facilities were used to assess quality of life and the extent to which operators have implemented newly revised municipal regulations. The findings are compared with those of earlier researchers (Taylor, Elliott, & Kearns, 1989) who conducted a similar survey in 1986 in Hamilton. While the two surveys differ with regard to specific items, broad comparison suggests little has changed, raising questions about the extent to which such facilities should continue to play a central role in housing for people with psychiatric disabilities.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.051
Threshold uncertainty score0.372

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0160.004
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.080
GPT teacher head0.409
Teacher spread0.329 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2009
Admission routes3
Has abstractyes

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