Rasch Measurement Theory's contribution to the psychometric properties of a co-created measure of health and wellness for Indigenous children and youth
Bibliographic record
Abstract
OBJECTIVES: To determine how Rasch Measurement Theory (RMT) methods can be used to assess the psychometric properties of the Aaniish Naa Gegii: the Children's Health and Wellbeing Measure (ACHWM) and Qanuippit. STUDY DESIGN AND SETTING: Indigenous children aged 8-18 years, from five communities, completed the 62-item ACHWM. We applied RMT methods to ACHWM data from 401 children (mean age 13.4 ± 3.4 years; 51% male) from across Ontario to examine how well the items captured the full range (±3 logit) of the concept of interest in each domain, targeted the needs of Indigenous children, and met the criteria for unidimensional and invariant measurement. RESULTS: = 809, P < 0.001). This model was further improved by aggregating the five response categories into three categories. All four domains showed excellent overall fit to the Rasch model (P > 0.05), with items covering between -4.51 and 6.02 logit, with no gaps along the theoretical continua. CONCLUSION: This study provides evidence that a set of conceptually derived items was able to produce a measure that fits the Rasch model. These results aid our understanding of wellness by establishing the clinical meaning of the scale scores.
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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 | 0.076 | 0.293 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".