Acceptance of a French e–Mental Health Information Website (CléPsy) for Families: A Web-Based Survey
Bibliographic record
Abstract
Background: Childhood mental health issues concern a large amount of children worldwide and represent a major public health challenge. The lack of knowledge among parents and caregivers in this area hinders effective management. Empowering families enhances their ability to address their children's difficulties, boosts health literacy, and promotes positive changes. However, seeking reliable mental health information remains challenging due to fear, stigma, and mistrust of the sources of information. Objective: This study evaluates the acceptance of a website, CléPsy, designed to provide reliable information and practical tools for families concerned about child mental health and parenting. Methods: This study examines user characteristics and assesses ease of use, usefulness, trustworthiness, and attitude toward using the website. Platform users were given access to a self-administered questionnaire by means of mailing lists, social networks, and posters between May and July 2022. Results: Findings indicate that the wide majority of the 317 responders agreed or somewhat agreed that the website made discussions about mental health easier with professionals (n=264, 83.3%) or with their relatives (n=260, 82.1%). According to the ANOVA, there was a significant effect between educational level and perceived trust (F6=3.03; P=.007) and between frequency of use and perceived usefulness (F2=4.85; P=.008). Conclusions: The study underlines the importance of user experience and design in web-based health information dissemination and emphasizes the need for accessible and evidence-based information. Although the study has limitations, it provides preliminary support for the acceptability and usefulness of the website. Future efforts should focus on inclusive co-construction with users and addressing the information needs of families from diverse cultural and educational backgrounds.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".