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Record W4408239515 · doi:10.1080/0886571x.2025.2467106

Claiming the edge as central: what residential childcare workers need to better serve children and families

2025· article· en· W4408239515 on OpenAlexaff
Denise Michelle Brend, Delphine Collin‐Vézina, Isabelle V. Daignault

Bibliographic record

VenueResidential Treatment for Children & Youth · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsUniversité de MontréalMcGill UniversityUniversité Laval
Fundersnot available
KeywordsEnhanced Data Rates for GSM EvolutionPsychologyDevelopmental psychologySociologyEconomic growthPolitical scienceComputer scienceEconomics

Abstract

fetched live from OpenAlex

Longstanding turnover of residential childcare workers (RCW) poses an important challenge to safeguarding the wellbeing of maltreated children and youth internationally. RCW instability results in negative impacts on children and youth in residential placement and is indicative of the threats posed by residential care work to RCW themselves. A better understanding of RCW’s subjective needs might shed light on which strategies to prioritize toward improving their working conditions. Researchers with longstanding professional experience in residential care settings interviewed 81 RCW to ask what supports were lacking in their day-to-day work. Their responses were thematically analyzed and categorized into a thematic structure representing multiple needs, the most important of which were related to organization factors. The organization, interpersonal, and professional support RCW describe as lacking demonstrate the need for a shift in paradigm at the structural and systemic levels of child welfare and youth justice. This paper offers evidence-based strategies to enact that change with the aim of promoting the wellbeing of RCW toward reducing turnover and improving care for children and youth in care.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.283
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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