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Record W3163184016 · doi:10.7870/cjcmh-2020-028

Array of Services for Homeless Mentally Ill in Six Canadian Cities: Non-Governmental Organizations’ Contributions and Perspectives

2020· article· en· W3163184016 on OpenAlexaffvenueabout
Alain Lesage, Carol E. Adair, Marie‐Josée Fleury, Guy Grenier, Charles Gaucher, Tim Aubry, Carolyn S. Dewa, Michelle Patterson, Julian M. Somers, Paula Goering

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsSimon Fraser UniversityUniversity of TorontoUniversity of OttawaUniversité de MonctonDouglas Mental Health University InstituteDouglas CollegeMcGill UniversityUniversity of CalgaryUniversité de Montréal
Fundersnot available
KeywordsMentally illMental healthMental illnessBusinessPerspective (graphical)Service (business)Social WelfareAddictionPublic relationsEconomic growthPolitical scienceMedicinePsychiatryMarketing

Abstract

fetched live from OpenAlex

During the period 2010–2011, when the At Home project was conducted, a questionnaire was sent to 420 non-governmental organization (NGO) key managers in six Canadian cities to enquire about their collaboration with public services and their perspective on the services for homeless people with serious mental illness (SMI). NGOs constituted a dense network of collaboration among themselves. With regard to public services, housing and shelters were two services that NGOs had frequent contact with, followed by the healthcare addiction sectors and, to a lesser extent, social service and the justice sectors. Education and employment were both located in the network periphery. In general, NGOs viewed housing availability and accessibility to health services as largely unsatisfactory. They called for better public support, coordination, and funding.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.091
Threshold uncertainty score0.659

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0250.005
Scholarly communication0.0070.001
Open science0.0020.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.036
GPT teacher head0.360
Teacher spread0.324 · 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

Citations0
Published2020
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Community Mental HealthSame topicHomelessness and Social IssuesFrench-language works237,207