Implementation of a Novel Epidemiological Surveillance System for Children’s Mental Health and Well-Being in France: Protocol for the National “Enabee” Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Children's mental health, including their well-being, is a major public health concern, as the burden of related disorders may last throughout one's life. Although epidemiological mental health surveillance systems for children and adolescents have been implemented in several countries, they are sorely lacking in France. OBJECTIVE: This study aims to describe the first step of the implementation of a novel surveillance system in France called Enabee (Etude nationale sur le bien-être des enfants), which focuses on the issue of mental health in children. The system aims to (1) describe the temporal trends in the population-based prevalence of the main mental health disorders and well-being in children aged 3 to 11 years, (2) explore their major determinants, and (3) assess mental health care use by this population. To do this, Enabee will rely on results from a recurrent national cross-sectional homonymous study. This paper presents the protocol for the first edition of this study (called Enabee 2022), as well as initial results regarding participation. METHODS: Enabee 2022 is a national cross-sectional study that was implemented in French schools in 2022. It used a probabilistic, multistage, stratified, and balanced sampling plan as follows: first, schools were randomly drawn and stratified according to the type of school. Up to 4 classes per school were then randomly drawn, and finally, all the pupils within each class were selected. The study covered children from preschool and kindergarten (aged 3 to 6 years, US grading system) to fifth grade (aged 6 to 11 years). Children from first to fifth grades provided a self-assessment of their mental health using 2 validated self-administered questionnaires: the Dominic Interactive (DI) and the KINDL. Parents and teachers completed a web-based questionnaire, including the Strengths and Difficulties Questionnaire. Parents also answered additional questions about their parenting attitudes; their own mental health; known social, economic, and environmental determinants of mental health in children; and their child's life habits. Health, education, and family stakeholders were involved in designing and implementing the study as part of a large consultation group. RESULTS: Data were collected from May 2, 2022, to July 31, 2022, in 399 schools across metropolitan France. Teachers completed questionnaires for 5721 pupils in preschool and kindergarten and for 15,263 pupils from first to fifth grades. Parents completed questionnaires for 3785 children in preschool and kindergarten and for 9227 children from first to fifth grades. Finally, 15,206 children from first to fifth grades completed the self-administered questionnaire. CONCLUSIONS: Enabee 2022 constitutes the first milestone in the development of a novel national epidemiological surveillance system, paving the way for improved children's mental health policies in France.
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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 | 0.054 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".