What child and adolescent psychiatry in France for the second quarter of the 21 <sup>st</sup> century? An AI-assisted qualitative action research study
Bibliographic record
Abstract
Abstract French Child and Adolescent Psychiatry (CAP) faces significant issues, primarily due to an overwhelming increase in demand and insufficient capacity. In response, the French Society for Child and Adolescent Psychiatry and Allied Professions (SFPEADA) initiated an action research project in June 2023 aimed at reimagining the future of CAP in France for the second quarter of the 21st century. Employing a holistic qualitative methodology that merges bottom-up and top-down approaches, the project progressed through four phases: interviews with informed individuals, consultations with trade unions or associations, synthesis of findings using thematic analysis and AI technologies, and public dissemination via a symposium at the ministry of health. The project identified 5 main themes: “CAP and Society”, “Knowledge Integration”, ”Healthcare Delivery”, “Caregivers”, ”System Organization”. This initiative underscores the importance of a collaborative, multidisciplinary approach to address the important needs of child and adolescent mental health in France, advocating for significant systemic changes to enhance CAP’s efficacy and accessibility.
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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 | 0.016 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".