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Record W3042630323 · doi:10.7202/1069983ar

ALLIANCE THÉRAPEUTIQUE ET SERVICES DE PROTECTION DE LA JEUNESSE : POINTS DE VUE ET EXPÉRIENCES DE JEUNES ISSUS DE MINORITÉS ETHNOCULTURELLES ET DE LEURS INTERVENANTS

2020· article· fr· W3042630323 on OpenAlexvenueaboutno aff
Gary Saint-Jean, Sarah Dufour

Bibliographic record

VenueCanadian social work review · 2020
Typearticle
Languagefr
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesAlliancePolitical scienceArt

Abstract

fetched live from OpenAlex

La surreprésentation de certains groupes de jeunes de minorités ethnoculturelles (MEC) dans le système de protection de la jeunesse est bien documentée au Québec. Parmi les solutions relevées pour y pallier, la relation intervenant-receveur de services a été identifiée comme un des meilleurs moteurs de changement. La présente étude qualitative phénoménologique vise à (1) comprendre davantage l’influence perçue de l’appartenance ethnoculturelle sur l’établissement de l’alliance thérapeutique entre des jeunes de MEC et leurs intervenants en protection; et (2) à comprendre, dans une perspective dyadique, les ressemblances, les différences, les points d’entente et les points de désaccord dans la dyade jeune-intervenant. Quinze jeunes de MEC âgés entre 12 et 17 ans et leurs treize intervenants ont été rencontrés lors d’entrevues semi-structurées. Les résultats permettent d’identifier des facteurs qui viennent influencer des paramètres de l’alliance de manière plus précise, notamment le lien affectif et l’accord sur les buts et les moyens. Ces influences semblent toutefois dépendre de l’importance relative du paramètre culturel dans la relation, mettant en lumière l’importance de l’évaluation préalable de cette dimension. Les implications pour la pratique clinique, la formation et la recherche 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 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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.819
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
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 head0.111
GPT teacher head0.436
Teacher spread0.324 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2020
Admission routes2
Has abstractyes

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