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Record W7038334816

Homelessness & Health in Canada

2017· book· en· W7038334816 on OpenAlexaboutno aff

Bibliographic record

VenueOpenEdition (OpenEdition) · 2017
Typebook
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsBlueprintPublic healthMental healthPsychological interventionRefugeePublic policyMental illness
DOInot available

Abstract

fetched live from OpenAlex

Homelessness & Health in Canada explores, for the first time, the social, structural, and environmental factors that shape the health of homeless persons in Canada. Covering a wide range of topics from youth homelessness to end-of-life care, the authors strive to outline policy and practice recommendations to respond to the ongoing public health crisis. This book is divided into three distinct but complimentary sections. In the first section, contributors explore how homelessness affects the health of particular homeless populations, focusing on the experiences of homeless youth, immigrants, refugees and people of Aboriginal ancestry. In the second section, contributors investigate how housing and public health policy as well as programmatic responses can address various health challenges, including severe mental illness and HIV/AIDS. In the final section, contributors highlight innovative Canadian interventions that have shown great promise in the field. Together, they form a comprehensive survey of an all too important topic and serve as a blueprint for action.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.069
Threshold uncertainty score0.502

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0110.003
Scholarly communication0.0080.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0300.006

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 head0.053
GPT teacher head0.364
Teacher spread0.311 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

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