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Record W2329954866 · doi:10.2975/35.4.2012.333.335

Recovery through the lens of cultural diversity.

2012· article· en· W2329954866 on OpenAlexaffabout
Nora Jacobson, Deqa Farah

Bibliographic record

VenuePsychiatric Rehabilitation Journal · 2012
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsDiversity (politics)Focus groupService providerMental healthCultural diversityService (business)Public relationsCultural competenceCulturally appropriateNursingPsychologySociologyMedicineBusinessPolitical scienceGerontologyMarketingPedagogyPsychiatry

Abstract

fetched live from OpenAlex

TOPIC: This Brief Report is based on a project entitled Recovery through the Lens of Cultural Diversity that sought to develop an enhanced model of recovery that accounts for culture. PURPOSE: The purpose of the project was to increase the ability of mental health organizations in Toronto, Canada to design and deliver culturally relevant and responsive recovery-oriented services to the diverse communities they serve. SOURCES USED: For the purposes of this report, sources used included an interdisciplinary and culturally diverse group of service providers; focus groups that included service users and family members; and a community forum. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: To assist practitioners to better meet the service needs of their culturally diverse service recipients, recommendations are available to guide the continuing advancement of culturally relevant recovery-promoting practices for service users and family members.

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)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.001
Science and technology studies0.0160.058
Scholarly communication0.0120.011
Open science0.0020.016
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.169
GPT teacher head0.425
Teacher spread0.256 · 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

Citations25
Published2012
Admission routes2
Has abstractyes

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