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Record W4237001406 · doi:10.24124/2016/bpgub1155

Web of culture: critically assessing and building culturally relevant online mental health resources for aboriginal youth in northern BC

2016· dissertation· en· W4237001406 on OpenAlexaff
Valerie Ward

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsUniversity of GuelphUniversity of Northern British Columbia
Fundersnot available
KeywordsParticipatory action researchMental healthDigital storytellingStorytellingThe InternetThe artsCitizen journalismAction researchCommunity-based participatory researchSociologyPsychologyPublic relationsPedagogyPolitical scienceWorld Wide WebComputer scienceNarrative

Abstract

fetched live from OpenAlex

Traditional sources of health information are no longer meeting the needs of younger generations, including Aboriginal youth, who are increasingly turning to the Internet with their health-related questions. Research has shown that culturally tailored health education and information resources are those best received by Aboriginal peoples. This project will look at whether existing online mental health resources are age and culturally appropriate for Aboriginal youth (ages 19-25) living in Northern BC. Using a social determinants of health framework, this research employed decolonizing and (participatory) action-based research methodologies, as well as arts-based methods (digital storytelling). Five key findings resulted from this study. The most important finding was that existing online mental health resources do not adequately address needs of Aboriginal youth living in Northern BC. Digital storytelling as an arts-based method, however, was an effective and engaging research tool to work with youth populations. --Leaf ii.

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)0.020
metaresearch head score (Gemma)0.035
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 score0.756
Threshold uncertainty score0.485

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.035
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0110.005
Scholarly communication0.0070.003
Open science0.0020.008
Research integrity0.0010.002
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 head0.035
GPT teacher head0.436
Teacher spread0.401 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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