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Record W4385456844 · doi:10.2196/51096

Improving Children’s Mental Health Literacy Through the Cocreation of an Intervention and Scale Validation: Protocol for the CHILD-Mental Health Literacy Research Study

2023· article· en· W4385456844 on OpenAlexaffvenue
Florence Francis-Oliviero, Céline Loubières, Christine Grové, Alexandra Marinucci, Rébecca Shankland, R. Salamon, Emmanuelle Perez, Laure Garancher, Cédric Galéra, E Gaillard, Massimiliano Orri, Juan Luis González-Caballero, Ilaria Montagni

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
FundersInstitut pour la Recherche en Santé Publique
KeywordsMental healthMental health literacyIntervention (counseling)Scale (ratio)LiteracyProtocol (science)Health literacyPsychologyMedical educationMedicineApplied psychologyPsychiatryMental illnessAlternative medicineHealth carePedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: Children's mental health is a public health priority, with 1 in 5 European children younger than 12 years having a behavioral, developmental, or psychological disorder. Mental health literacy (MHL) is a modifiable determinant of mental health, promoting psychological well-being and reducing mental health problems. Despite its significance, no interventions or scales currently exist for increasing and measuring MHL in this population. OBJECTIVE: This study has dual objectives: (1) cocreating and evaluating an intervention on children's MHL, and (2) developing and validating a scale that measures children's MHL. METHODS: Our study focuses on children aged 9-11 years attending primary school classes in various settings, including urban and rural areas, and priority education zones within a French department. Using a participatory research approach, we will conduct workshops involving children, parents, teachers, and 1 artist to cocreate an intervention comprising multiple tools (eg, a pedagogical kit and videos). This intervention will undergo initial evaluation in 4 classes through observations, interviews, and satisfaction questionnaires to assess its viability. Concurrently, the artist will collaborate with children to create the initial version of the CHILD-MHL scale, which will then be administered to 300 children. Psychometric analyses will validate the scale. Subsequently, we will conduct a cluster randomized controlled trial involving a minimum of 20 classes, using the CHILD-MHL scale scores as the primary end point to evaluate the intervention's efficacy. Additional interviews will complement this mixed methods evaluation. Both the intervention and the scale are grounded in the Child-Focused MHL model. RESULTS: The first tool of the intervention is the pedagogical kit Le Jardin du Dedans, supported by the public organization Psycom Santé Mentale Info and endorsed by UNICEF (United Nations Children's Fund) France. The second tool is a handbook by the Pan American Health Organization and the World Health Organization, which is addressed to teachers to sensitize them to children's mental health problems. The third is a 5-page supplementary leaflet produced by the nongovernmental organization The Ink Link, which teaches children the notion of MHL. Finally, we produced 56 items of the MHL Scale and listed existing education policies for children's mental health. CONCLUSIONS: After its robust evaluation, the intervention could be extended to several schools in France. The scale will be the first in the world to measure children's MHL. It will be used not only to evaluate interventions but also to provide data for decision makers to include MHL in all educational policies. Both the intervention and the scale could be translated into other languages. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/51096.

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 imitation

Not 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.

metaresearch head score (Codex)0.055
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.062
Threshold uncertainty score0.288

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0550.036
Meta-epidemiology (narrow)0.0060.005
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0050.005
Science and technology studies0.0060.004
Scholarly communication0.0030.003
Open science0.0050.004
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0620.015

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.

Opus teacher head0.295
GPT teacher head0.672
Teacher spread0.376 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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".

Quick stats

Citations5
Published2023
Admission routes2
Has abstractyes

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