MétaCan
Menu
Back to cohort
Record W4229049276 · doi:10.1177/1077559520979588

An Examination of Past Trends in School Reports to Child Welfare: Considerations for Reported Child Maltreatment

2020· article· en· W4229049276 on OpenAlexafffundabout
Barbara Fallon, Joanne Filippelli, Nicolette Joh-Carnella, Delphine Collin‐Vézina, Rachael Lefebvre, Brenda Moody, Nico Trocmé, Ashley Quinn

Bibliographic record

VenueChild Maltreatment · 2020
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsChildren's Aid SocietyMcGill UniversityUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsWelfarePoison controlChild abuseInjury preventionSuicide preventionHuman factors and ergonomicsOccupational safety and healthPsychologyMedicineDevelopmental psychologyClinical psychologyMedical emergencyPolitical science

Abstract

fetched live from OpenAlex

This study examines whether increased interaction and observation of young children by school professionals leads to an increase in school-based reports to child welfare authorities and in the identification of child maltreatment victims. Comparing provincial-level data collected before and after full-day kindergarten implementation in Ontario, a doubling in rates of school-referred investigations involving 4- and 5-year-old children was found. There was no significant difference in the rates of maltreatment substantiation, service referrals made or transfers to ongoing services, but the rate of child functioning concerns noted in these investigations tripled. The findings suggest there are differences in how the school and child welfare systems define and respond to suspected child maltreatment. Implications for practice, policy and research are explored.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.458

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.306
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueChild MaltreatmentSame topicChild Abuse and TraumaFrench-language works237,207