Psychometric Properties of the Dominic Interactive in a Large French Sample
Bibliographic record
Abstract
OBJECTIVES: To examine the psychometric properties of the Dominic Interactive (DI) in school-aged children in a different cultural environment than Quebec. METHODS: In a large French region, 100 schools and 25 children (aged 6 to 11 years) per school were randomly selected. Data were collected using self-administered questionnaires to children (DI), parents (sociodemographic characteristics, mental health services use), and teachers (child school achievement). DI psychometric properties were assessed by examining: the distribution of each DI diagnosis; comorbidity between diagnoses; alpha coefficients measuring internal consistency; and correlates of psychopathologies with sociodemographic status and health care services use. Estimates of DI properties were compared with those from a sample of community children in Quebec. RESULTS: Complete data were available for 1274 children (54.4%). The internal consistency of each DI diagnosis of the French version was reasonable, with Cronbach's alpha coefficients ranging from 0.62 to 0.89. The psychometric properties and comorbidity were consistent with the version from Quebec. CONCLUSIONS: The satisfactory psychometric properties of the DI along with other demonstrated advantages of this instrument (children enjoy the activity, parents approve of it, and it is cost-effective) and its cultural adaptability support the consideration of the DI for epidemiologic studies in diverse cultures.
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 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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".