Development and Validation of the Middle Years Development Instrument (MDI): Assessing Children’s Well-Being and Assets across Multiple Contexts
Bibliographic record
Abstract
Few instruments provide reliable and valid data on child well-being and contextual assets during middle childhood, using children as informants. The authors developed a population-level, self-report measure of school-aged children's well-being and assets-the Middle Years Development Instrument (MDI)-and examined its reliability and validity. The MDI was designed to assess child well-being inside and outside of school on five dimensions: (1) Social and emotional development, (2) Connectedness to peers and to adults at school, at home, and in the neighborhood, (3) School experiences, (4) Physical health and well-being, and (5) Constructive use of time after school. This paper describes the theoretical framework, selection of items and scales for the survey, and four studies that were conducted to revise the MDI and examine its psychometric properties. The findings indicate a theoretically predicted factor structure, high internal consistency, and document the convergent and discriminant validity of the MDI scales. The discussion delineates a plan for future validation studies that address further validity questions, such as predictive validity, measurement invariance, and fairness/bias, and provides a brief outlook of how the MDI may be used by practitioners, educators, and decision makers in schools and communities to motivate and inform action in support children's well-being.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".