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Record W2988976803 · doi:10.3917/rsi.138.0043

Intervenir sur les facteurs de risque de maltraitance infantile : quelle aisance chez les professionnels français de prévention précoce ?

2019· article· fr· W2988976803 on OpenAlexaff
Thomas Saïas, Samantha Kargakos, Julie Poissant, Charles Eury

Bibliographic record

VenueRecherche en soins infirmiers · 2019
Typearticle
Languagefr
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsMedicinePolitical science

Abstract

fetched live from OpenAlex

La recherche portant sur la prévention de la maltraitance est limitée depuis 40 ans en champ expérimental. Si l’on sait comment des actions à domicile peuvent contribuer à limiter les situations de mauvais traitement aux enfants, on ne connait pas les enjeux liés à la réplication de ces résultats dans les soins courants. Cette étude avait pour objectif d’identifier, dans les services publics français, comment les professionnels de prévention abordent les facteurs de risque de maltraitance avec les familles, et avec quelle aisance. À travers ces notions, ce sont les compétences professionnelles qui sont interrogées, à la lumière des missions confiées à ces professionnels de prévention. Les résultats montrent que, au-delà d’une forme de polyvalence leur permettant d’aborder un large spectre de sujets avec les familles, les intervenants de prévention se déclarent très peu à l’aise pour traiter les sujets d’intimité familiale, de psychopathologie ou de violence envers les enfants. Les implications concernant les politiques de prévention de la maltraitance sont discutées.

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)0.007
metaresearch head score (Gemma)0.017
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 score0.253
Threshold uncertainty score0.503

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.118
GPT teacher head0.397
Teacher spread0.278 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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