Supporting families in challenging contexts: the CAPEDP project
Bibliographic record
Abstract
Although France has one of the most generous health and social care systems for infant and maternal well-being in the Western world, professionals have been increasingly concerned by the rising number of children being referred for mental health problems. The present article describes the first home-visiting program in France to specifically target mental health questions in families living in vulnerable contexts. The CAPEDP project, involving 440 women and their families, took place in Paris and its inner suburbs from 2006 to 2011. To be eligible for inclusion, women had to be (i) under 26 years old, (ii) less that 27 weeks pregnant, (iii) sufficiently fluent in French to give truly informed consent to participate in the study and benefit from the intervention and (iv) presenting with one or more of the following social vulnerability factors: low income, low educational level, and/or intending to bring up the child without the child's father. The intervention consisted of 44 home visits from the third trimester of pregnancy through to the child's second birthday. The aim of the intervention was to promote infant mental health and reduce the incidence of infant mental health problems at the age of two years. The intervention paid particular attention to postnatal maternal depression and promoting parenting skills and attachment security, particularly through the use of video during home-visits. A major issue was that of adapting international best practice recommendations with regard to home-visiting programs to the particularities of the existing French social and health care system. An original aspect of the intervention was to use trained clinical psychologists to conduct all home visits.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".