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Record W2606993770 · doi:10.22605/rrh3941

Relevance of the Aboriginal Children’s Health and Well-being Measure Beyond Wiikwemkoong

2017· article· en· W2606993770 on OpenAlexafffundabout
Nancy L. Young, Mary Jo Wabano, Shannon Blight, Karen Baker-Anderson, Roger Beaudin, Leslie F. McGregor, Lorrilee McGregor, Tricia A. Burke

Bibliographic record

VenueRural and Remote Health · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsRainbow Health OntarioInuit Tapiriit KanatamiAssembly of First NationsLaurentian University
FundersCanadian Institutes of Health Research
KeywordsRelevance (law)MainstreamAgency (philosophy)Public relationsPsychologySociologyMedical educationGerontologyMedicinePolitical scienceSocial science

Abstract

fetched live from OpenAlex

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.

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)0.017
metaresearch head score (Gemma)0.037
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 score0.510
Threshold uncertainty score0.975

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0040.003
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.008
GPT teacher head0.307
Teacher spread0.298 · 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".

Quick stats

Citations14
Published2017
Admission routes3
Has abstractyes

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