Reliability of the Aboriginal Children’s Health and Well-Being Measure (ACHWM)
Bibliographic record
Abstract
PURPOSE: The aim of this research was to evaluate the reliability of the Aboriginal Children's Health and Well-Being Measure© (ACHWM). METHODS: Two cohorts of children from Wiikwemkoong Unceded Territory were recruited for this study. Each child completed the ACHWM independently on a computer tablet running a customized survey app. The data from the first and second cohorts were used to estimate the internal consistencies using Cronbach's alpha. A subgroup of the second cohort completed the survey twice, within the same day. The data from this subgroup was used to evaluate the test-retest reliability using a random effects Intra-class Correlation Coefficient (ICC). RESULTS: There were 124 participants in the first cohort and 132 participants in the second cohort. The repeated measures subgroup was comprised of 29 participants from the second cohort. The internal consistency statistic (Cronbach's alpha) was 0.93 for the first and second cohorts. The test-retest reliability ICC was 0.94 (95% CI 0.86-0.97) for the ACHWM summary scores based on the repeated measures subgroup. CONCLUSIONS: These results establish the internal consistency and the test-retest validity of the ACHWM. This important finding will enable Aboriginal communities to use this measure with confidence and promote the voices of their children in reporting their health. The ACHWM is an essential data gathering tool that enables evidence-based health care for Aboriginal communities.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".