Validity of the Aboriginal Children’s Health and Well-being Measure: Aaniish Naa Gegii?
Bibliographic record
Abstract
BACKGROUND: Aboriginal children experience challenges to their health and well-being, yet also have unique strengths. It has been difficult to accurately assess their health outcomes due to the lack of culturally relevant measures. The Aboriginal Children's Health and Well-Being Measure (ACHWM) was developed to address this gap. This paper describes the validity of the new measure. METHODS: We recruited First Nations children from one First Nation reserve in Canada. Participants were asked to complete the ACHWM independently using a computer tablet. Participants also completed the PedsQL. The ACHWM total score and 4 Quadrant scores were expected to have a moderate correlation of between 0.4 and 0.6 with the parallel PedsQL total score, domains (scale scores), and summary scores. RESULTS: Paired ACHWM and PedsQL scores were available for 48 participants. They had a mean age of 14.6 (range of 7 to 19) years and 60.4 % were girls. The Pearson's correlation between the total ACHWM score and a total PedsQL aggregate score was 0.52 (p = 0.0001). The correlations with the Physical Health Summary Scores and the Psychosocial Health Summary Scores were slightly lower range (r = 0.35 p = 0.016; and r = 0.51 p = 0.0002 respectively) and approached the expected range. The ACHWM Quadrant scores were moderately correlated with the parallel PedsQL domains ranging from r = 0.45 to r = 0.64 (p ≤ 0.001). The Spiritual Quadrant of the ACHWM did not have a parallel domain in the PedsQL. CONCLUSIONS: These results establish the validity of the ACHWM. The children gave this measure an Ojibway name, Aaniish Naa Gegii, meaning "how are you?". This measure is now ready for implementation, and will contribute to a better understanding of the health of Aboriginal children.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.003 | 0.001 |
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".