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Record W2766935968 · doi:10.1606/1044-3894.2017.98.35

Our Biggest Hurdle Yet: Caregivers' Encounters with Structural Stigma in Child and Youth Mental Health

2017· article· en· W2766935968 on OpenAlexaff
Maria Liegghio

Bibliographic record

VenueFamilies in Society The Journal of Contemporary Social Services · 2017
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsYork University
Fundersnot available
KeywordsMental healthStigma (botany)Mental health serviceFocus groupPsychologySocial stigmaCriminal justiceMental illnessPsychiatryMedicineCriminologyFamily medicineSociology

Abstract

fetched live from OpenAlex

The aim of this article explores structural stigma in child and youth mental health. The focus is on the experiences of caregivers with children between 12 and 22 years old in their encounters with the systems involved with their families for their children's mental health. The outcomes are presented of a study based on analysis of 10 transcripts—two focus group and eight interview transcripts—with 15 caregivers with children involved with mental health services. Two main themes emerged: (a) structural stigma as denigrating encounters within and across service systems (i.e., child and youth mental health, education, child protection, and criminal justice) and (b) fragmentation of the service delivery system as a significant factor exacerbating stigmatizing encounters. The implications for mental health practice with families are considered.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0150.010
Scholarly communication0.0050.005
Open science0.0010.010
Research integrity0.0020.005
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 head0.022
GPT teacher head0.290
Teacher spread0.268 · 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 designQualitative
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

Citations10
Published2017
Admission routes1
Has abstractyes

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