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Record W2949179306 · doi:10.7202/1060004ar

L’utilisation d’outils standardisés en intervention sociale : les points de vue des intervenants, des gestionnaires et des familles sur le Protocole d’évaluation familiale en protection de la jeunesse

2019· article· fr· W2949179306 on OpenAlexaffvenueabout
Doris Châteauneuf, Catherine Arseneault, Marie-Ève Bédard Nadeau

Bibliographic record

VenueRevue de psychoéducation · 2019
Typearticle
Languagefr
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité du Québec en OutaouaisCentre Jeunesse de Quebec
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Malgré la promotion et la valorisation des données probantes dans les domaines de la santé et des services sociaux, l’utilisation d’outils d’évaluation systématique en intervention sociale demeure un sujet de recherche peu exploré au Québec. Le présent article s’intéresse aux enjeux entourant l’implantation et l’utilisation d’un outil d’évaluation systématique en contexte d’intervention en protection de la jeunesse, le Protocole d’évaluation familiale (PEF). À partir des points de vue de 37 acteurs directement concernés par l’utilisation de ce protocole (intervenants, gestionnaires et familles), cette étude se penche plus spécifiquement sur les enjeux qui interviennent dans le processus d’implantation de l’outil et qui influencent son utilisation. Les résultats indiquent que le contexte organisationnel, les propriétés des questionnaires, les caractéristiques des acteurs, ainsi que les perceptions de ces derniers à l’égard de la pertinence clinique de l’outil sont les principaux facteurs soulevés par les participants pour expliquer les conditions d’implantation et le niveau d’utilisation du PEF.

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)0.024
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.146
GPT teacher head0.464
Teacher spread0.318 · 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

Citations0
Published2019
Admission routes3
Has abstractyes

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