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Record W4294243617 · doi:10.23889/ijpds.v7i3.1961

Linking Databases in Collaborative and Culturally Safe Ways to Evaluate the Effectiveness of PAX-Good Behaviour Game (PAX) in First Nations Communities.

2022· article· en· W4294243617 on OpenAlexaffabout
Mariette Chartier, Garry Munro, Nora Murdock, Frank M. Turner, Leanne Boyd, Laurence M. Katz, Marni Brownell, Depeng Jiang, Amanda Martinson, Janique Fortier, Jitender Sareen

Bibliographic record

VenueInternational Journal for Population Data Science · 2022
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsCree Board of Health and Social Services of James BayGovernment of ManitobaUniversity of ManitobaFirst Nations Health and Social Secretariat of ManitobaManitoba Health
Fundersnot available
KeywordsIndigenousPublic relationsPromotion (chess)Research ethicsSociologyPsychologyPolitical scienceLawEcologyPolitics

Abstract

fetched live from OpenAlex

ObjectivesThe overarching research objective was to examine, culturally adapt, and further evaluate a mental health promotion approach called the PAX within 8 First Nations communities. This presentation describes a research process whereby First Nations community members and researchers worked in collaborative and culturally safe ways to reach their research objectives. ApproachBuilding on a strong existing relationship between Swampy Cree Tribal Council (SCTC) members from Northern Canada and academic researchers, a team was formed to prepare the research proposal. This team included community members, leaders from First Nations organizations, decision makers, program developers and researchers. This research was guided by two-eyed seeing, a principle developed by a Mi’kmaw Elder, that recognizes both Indigenous and Western ways of knowing, where one worldview does not dominate the other. The research process was compliant with Ownership, Control, Access, and Possession (OCAP) principles that ensure self-determination of First Nations communities over research involving their people. ResultsA First Nations community liaison was hired as a research team member ensuring that traditional and cultural protocols were adhered to and connections to community members facilitated and sustained. Over the course of the research, the team met monthly to oversee implementation and annually with SCTC community members for guidance and for sharing and interpreting results. All 8 communities were actively engaged and benefitted from their involvement. Seeing the value of examining PAX’s effectiveness through linkages to administrative datasets, community members supported engagement of an additional 16 First Nations communities thereby ensuring an adequate sample size for the study. Health and education databases were linked to program data from 20 First Nations communities. Infographics, lay summaries and presentations were prepared for meaningful knowledge exchange. ConclusionFirst Nations communities deemed it essential to understand what works and for whom regarding mental health promotion. Building relationships with First Nations community members based on trust and respect provided information that was relevant and beneficial to their communities. This relationship-building should be considered when developing research timelines and budgets.

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)0.053
metaresearch head score (Gemma)0.121
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 score0.982
Threshold uncertainty score0.281

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0530.121
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0050.007
Open science0.0030.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.189
GPT teacher head0.504
Teacher spread0.314 · 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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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