Adapting and enhancing PAX Good Behavior Game for First Nations communities: a mixed-methods study protocol developed with Swampy Cree Tribal Council communities in Manitoba
Bibliographic record
Abstract
INTRODUCTION: High rates of mental health problems, such as suicidal behaviours, among First Nations youth in Canada are a major public health concern. The Good Behavior Game (GBG) is a school-based intervention that provides a nurturing environment for children and has been shown to promote positive outcomes. PAX Good Behavior Game (PAX GBG) is an adaptation and enhancement of the GBG. While PAX GBG has been implemented in Indigenous communities, little research exists examining the cultural and contextual appropriateness and effectiveness of the intervention in First Nations communities. METHODS AND ANALYSIS: The present paper describes a protocol of the mixed-methods approach guided by an Indigenous ethical engagement model adopted to implement, adapt and evaluate PAX GBG in First Nations communities in Manitoba, Canada. First, implementation outcomes (eg, acceptability, adoption) of PAX GBG will be evaluated using qualitative interviews with teachers, principals and community members from Swampy Cree Tribal Council (SCTC) communities. Second, by linking administrative databases to programme data from schools in 38 First Nations communities, we will compare PAX GBG and control groups to evaluate whether PAX GBG is associated with improved mental health and academic outcomes. Third, the qualitative results will help inform a cultural and contextual adaptation of PAX GBG called First Nations PAX (FN PAX). Fourth, FN PAX will be implemented in a few SCTC communities and evaluated using surveys and qualitative interviews followed by the remaining communities the subsequent year. ETHICS AND DISSEMINATION: Ethical approval was obtained from the University of Manitoba Health Research Ethics Board and will be obtained from the Health Information Privacy Committee and respective data providers for the administrative database linkages. Dissemination and knowledge translation will include community and stakeholder engagement throughout the research process, reports and presentations for policymakers and community members, presentations at scientific conferences and journal publications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".