Relevance of the Aboriginal Children’s Health and Well-being Measure Beyond Wiikwemkoong
Bibliographic record
Abstract
INTRODUCTION: The Aboriginal Children's Health and Well-being Measure (ACHWM) was developed to meet the need for a culturally relevant measure of health and wellbeing for Aboriginal children (ages 8-18 years) in Canada. It was developed within one First Nation community: the Wiikwemkoong Unceded Territory. The intention from inception was to ensure the feasibility and relevance of the ACHWM to other Aboriginal communities. The purpose of this article is to describe the relevance of the ACHWM beyond Wiikwemkoong. METHODS: This article presents the results of a community-based and collaborative research study that was jointly led by an academic researcher and a First Nations Health leader. The research began with the 58-question version of the ACHWM developed in Wiikwemkoong. The ACHWM was then submitted to a well-established process of community review in four new communities (in sequence): Weechi-it-te-win Family Services, M'Chigeeng First Nation, Whitefish River First Nation, and the Ottawa Inuit Children's Centre (OICC). The review process included an initial review by local experts, followed by a detailed review with children and caregivers through a detailed cognitive debriefing process. Each community/agency identified changes necessary to ensure appropriate fit in their community. The results from all communities were then aggregated and analysed to determine the similarities and differences. RESULTS: This research was conducted in 2014 and 2015 at four sites. Interviews with 23 children and 21 caregivers were completed. Key lessons were learned in all communities that enabled the team to improve the ACHWM in subtle but important ways. A total of 12 questions were revised, and four new questions were added during the process. This produced a 62-question version of the ACHWM, which was endorsed by all communities. CONCLUSIONS: The ACHWM has been improved through a detailed review process in four additional communities/agencies and resulted in a stable 62-question version of the survey. This process has demonstrated the relevance of the ACHWM to a variety of Aboriginal communities. This survey provides Aboriginal communities with a culturally appropriate tool to assess and track their children's health outcomes, enabling them to gather new evidence of child health needs and the effectiveness of programs in the future.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".