Asthma Education Program for First Nations Children: An exemplar of the Knowledge-to-Action Framework
Bibliographic record
Abstract
BACKGROUND: The prevalence of asthma in Aboriginal children is 6% to 14%. Gaps in knowledge regarding asthma and its management exist in First Nations (FN) communities, and culturally relevant education and resources are required. Studies have recommended that the children’s asthma education program, the ‘Roaring Adventures of Puff’, be modified through partnership with FN communities to be culturally appropriate.OBJECTIVE: To adapt this knowledge tool and design an effective implementation process for FN knowledge users (children with asthma and care providers), guided by the Canadian Institutes of Health Research knowledge translation framework.METHODS: The problem was identified, knowledge was identified/reviewed/selected (literature review); knowledge was adapted to the local context (FN working and advisory groups); barriers to knowledge use were assessed (by knowledge users); and interventions were selected, tailored and implemented (modified curricula and the creation of a new activity book and web-based resources, and regional coordinators, asthma educator mentors and community teams were recruited).RESULTS: Major outcomes were the adapted tools and blueprints for tailoring implementation. Additional outcomes were preliminary observations and outputs from the iterative processes, including information about local context and barriers. Specific additions were roles for community members supported by asthma educators (applying FN teaching models and addressing health care demands); relevant triggers (addressing knowledge gaps); and FN images and stories, themes of circle, sacred teachings, nature and family/elders (culture and addressing low reading levels).CONCLUSION: The framework model provides a logical, valuable tool for adapting a knowledge tool and implementation process to new knowledge users. Future research should measure uptake, effect on health outcomes of FN asthma sufferers and sustainability.
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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 | 0.020 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".