Une voix négligée : Les parents d'accueil partageant leurs expériences concernant l'instabilité de placement
Bibliographic record
Abstract
Il est estimé que, en 2013, 62,428 enfants étaient placés sous les soins du système de protection au Canada (Jones, Sinha & Trocmé, 2015). Certains enfants finissent par retourner avec leurs parents biologiques, tandis que d’autres demeurent en famille d’accueil ou sont adoptés. Selon Khoo et Skoog (2014), entre 20 et 40 % des enfants placés en famille d’accueil connaissent une perturbation de placement. La problématique des perturbations de placement est peu abordée sous la perspective des parents d’accueil. Cette recherche tente de mieux comprendre l’expérience des parents d’accueil en lien avec la problématique des perturbations de placement. Elle mobilise l’approche écologique et l’analyse phénoménologique interprétative (API) afin d’explorer comment les parents d’accueil vivent les perturbations de placement et les facteurs qui contribuent à ces perturbations. Finalement, cette recherche offre des recommandations dans l’optique de prévenir les perturbations de placement. Mots-clés : famille d’accueil, système de protection, placement, approche écologique, l’analyse phénoménologique interprétative (API), perturbation de placement et expérience personnelle. Researchers have estimated that, in 2013, 62,428 children were placed in the care of child protection services in Canada (Jones, Sinha & Trocmé, 2015). Some children end up returning to their birth parents, while others remain with foster families or are adopted. According to Khoo and Skoog (2014), between 20 and 40% of children placed in foster families experience disruption. However, the issue of placement disruptions is seldom addressed from the perspectives of foster parents. This research attempts to better understand the experiences of foster parents in relation to the problem of placement disruptions. It uses the ecological approach and the interpretative phenomenological analysis (IPA) to explore foster parents’ experiences of placement disruptions and better understand the factors that contribute to such disruptions. Finally, this research offers recommendations to prevent placement disruptions. Keywords: foster family, child protection system, placement, ecological approach, interpretive phenomenological analysis (IPA), placement breakdown, and personal experience.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".