Improving quality in childcare facilities, the “Accompagne-moi…!” approach: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Provided they are accessible and of high quality, childcare centres may help in addressing social inequalities in health. The "Accompagne-moi…!" (AM) approach was developed to improve the quality of these centres in France. It aimed at improving professional practices to help children develop their social and emotional skills, and was evaluated in a pilot study which analysed its viability conditions in France. METHODS: A qualitative study was conducted in 14 childcare centres of four deprived areas in France over the 2019-2022 period. Eight interviews were conducted with stakeholders who initiated the approach, followed by 33 semi-structured interviews with childcare professionals. RESULTS: The childcare centres interviewed were characterised by involved professionals working in challenging conditions, which were exacerbated during the health crisis. AM had the potential to meet certain needs (e.g. professional training) and to produce positive effects (e.g. a less stressful environment). However, the challenges of deploying such an intervention in highly stressed environments resulted in some professionals reporting a negative experience, such as a feeling of failure or loneliness. The viability conditions of AM were therefore highlighted, such as the need for sufficient human resources, integration of the approach into childcare centre projects, collective ownership of the approach, and supportive management and organisation. CONCLUSION: While it is essential to consider the emotions of the children in childcare centres, the results emphasise how the well-being of children must be considered alongside to the well-being of professionals. Although it needs to be adapted before being scaled up, the AM approach has the potential to contribute to high-quality early childhood education. Some structural determinants are essential to implement interventions such as AM without creating difficulties for professionals.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| 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".