School personnel and community members’ perspectives in implementing PAX good behaviour game in first nations grade 1 classrooms
Bibliographic record
Abstract
First Nations peoples in Canada have a history of poor mental health outcomes, as the result of colonisation and the legacy of residential schools. The PAX Good Behaviour Game (PAX-GBG) is a school-based intervention shown to improve student behaviour, academic outcomes, and reduce suicidal thoughts and actions. This study examines the use of PAX-GBG in First Nations Grade 1 classrooms in Manitoba. Researchers collected qualitative data via interviews and focus groups from 23 participants from Swampy Cree Tribal Council (SCTC) communities. Participants reported both positive effects and challenges of implementing PAX-GBG in their classrooms. PAX-GBG created a positive environment where children felt included, recognised, and empowered. Children were calmer, more on-task, and understood the behaviours that are expected of them. However, for many reasons, PAX-GBG is not being used consistently across SCTC schools. Participants described barriers in implementation due to teacher turnover, lack of on-going training and support, developmental and behavioural difficulties of students, and larger community challenges. Participants provided suggestions on how to improve PAX-GBG to be a better fit for these communities, including important cultural and contextual adaptations. PAX-GBG has the potential to improve outcomes for First Nations children, however attention must be given to implementation within community context.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".