BEST PRACTICE IN INDIVIDUAL SUPERVISION OF PSYCHOLOGISTS WORKING IN THE FRENCH CAPEDP PREVENTIVE PERINATAL HOME-VISITING PROGRAM: RESULTS OF A DELPHI CONSENSUS PROCESS
Bibliographic record
Abstract
Individual supervision of home-visiting professionals has proved to be a key element for perinatal home-visiting programs. Although studies have been published concerning quality criteria for supervision in North American contexts, little is known about this subject in other national settings. In the context of the CAPEDP program (Compétences parentales et Attachement dans la Petite Enfance: Diminution des risques liés aux troubles de santé mentale et Promotion de la résilience; Parental Skills and Attachment in Early Childhood: Reducing Mental Health Risks and Promoting Resilience), the first randomized controlled perinatal mental health promotion research program to take place in France, this article describes the results of a study using the Delphi consensus method to identify the program supervisors' points of view concerning best practice for the individual supervision of home visitors involved in such programs. The final 18 recommendations could be grouped into four general themes: the organization and setting of supervision sessions; supervisor competencies; relationship between supervisor and supervisee; and supervisor intervention strategies within the supervision process. The quality criteria identified in this perinatal home-visiting program in the French cultural context underline the importance of clinical supervision and not just reflective supervision when working with families with multiple, highly complex needs.
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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 | 0.158 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".