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Record W3097025249 · doi:10.1037/ort0000520

Adaption and implementation of the Housing Outreach Program Collaborative (HOP-C) North for Indigenous youth.

2020· article· en· W3097025249 on OpenAlexaff
Elaine Toombs, Christopher J. Mushquash, Jessie Lund, Victoria A. Pitura, Kaitlyn Toneguzzi, Scott Leon, Tina Bobinski, Nina Vitopoulos, Tyler Frederick, Sean A. Kidd

Bibliographic record

VenueAmerican Journal of Orthopsychiatry · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsOntario Tech UniversityCentre for Addiction and Mental HealthWellesley InstituteLakehead University
Fundersnot available
KeywordsOutreachIndigenousHop (telecommunications)PsychologyPolitical scienceSociologyTelecommunicationsEconomic growthComputer scienceEconomics

Abstract

fetched live from OpenAlex

= 14) engaged in implementing the program. After completing the HOP-C North program, participants reported improvements in a number of outcomes, including increased educational enrollment, attainment of employment, reduced hospitalizations, and increased engagement in clinical mental health services. Specific program aspects that participants found helpful included increased program flexibility, accessibility, emphasis on relationships, relevance of programming, fostering participant autonomy, and an adaptive approach to program implementation. These findings suggest that the HOP-C North model, when adapted, is a helpful program for Indigenous youth. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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)0.002
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: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0000.001
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 head0.032
GPT teacher head0.398
Teacher spread0.367 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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