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Record W2097900368 · doi:10.2105/ajph.2014.302481

Factors Influencing the Health and Wellness of Urban Aboriginal Youths in Canada: Insights of In-Service Professionals, Care Providers, and Stakeholders

2015· article· en· W2097900368 on OpenAlexafffundabout
Kyoung June Yi, Edwige Landais, Fariba Kolahdooz, Sangita Sharma

Bibliographic record

VenueAmerican Journal of Public Health · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Alberta
FundersPublic Health Agency of Canada
KeywordsService providerHealth professionalsEnvironmental healthService (business)NursingHealth careMedicineBusinessPolitical science

Abstract

fetched live from OpenAlex

We addressed the positive and negative factors that influence the health and wellness of urban Aboriginal youths in Canada and ways of restoring, promoting, and maintaining the health and wellness of this population. Fifty-three in-service professionals, care providers, and stakeholders participated in this study in which we employed the Glaserian grounded theory approach. We identified perceived positive and negative factors. Participants suggested 5 approaches-(1) youth based and youth driven, (2) community based and community driven, (3) culturally appropriate, (4) enabling and empowering, and (5) sustainable-as well as some practical strategies for the development and implementation of programs. We have provided empirical knowledge about barriers to and opportunities for improving health and wellness among urban Aboriginal youths in Canada.

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)0.004
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 score0.076
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.004
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.127
GPT teacher head0.395
Teacher spread0.268 · 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
Published2015
Admission routes3
Has abstractyes

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