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Record W2939851220 · doi:10.1177/1177180119841620

The rationale for developing a programme of services by and for Indigenous men in a First Nations community

2019· article· en· W2939851220 on OpenAlexafffundabout
Julie George, Melody E. Morton Ninomiya, Kathryn Graham, Sharon Bernards, Samantha Wells

Bibliographic record

VenueAlterNative An International Journal of Indigenous Peoples · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsCentre for Addiction and Mental Health
FundersCanadian Institutes of Health ResearchMovember CanadaMovember Foundation
KeywordsIndigenousMental healthPublic healthCulturally appropriateQualitative researchPsychologyGerontologyMedicineNursingSociologyPsychiatrySocial science

Abstract

fetched live from OpenAlex

While mental well-being is recognized as a significant public health priority in numerous Indigenous communities, little work has focused on the mental health needs of Indigenous men. In this article, we describe results from the mixed-methods research used to inform the development of mental wellness programming for boys and men. Quantitative and qualitative data from two studies conducted in Kettle & Stony Point First Nation, an Indigenous community in southern Ontario, Canada, were used to (a) understand factors that contributed to issues of mental health, substance use and violence for men, (b) understand men’s experiences accessing and seeking supports and services, and (c) identify ways to address mental health, substance use and violence among boys and men in the community. We show how results from two studies ignited a group of men to develop a culturally strong and strengths-based programme of services as well as a wellness strategy for boys and men in the community.

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)0.025
metaresearch head score (Gemma)0.016
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 score0.985
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0110.009
Scholarly communication0.0050.004
Open science0.0030.009
Research integrity0.0090.010
Insufficient payload (model declined to judge)0.0040.001

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 head0.029
GPT teacher head0.348
Teacher spread0.319 · 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

Citations11
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueAlterNative An International Journal of Indigenous PeoplesSame topicIndigenous Health, Education, and RightsFrench-language works237,207