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Record W4388952296 · doi:10.1111/1467-9566.13730

Epistemic injustice in experiences of young people with parents with mental health challenges

2023· article· en· W4388952296 on OpenAlexaff
Scott Yates, Brenda Gladstone, Kim Foster, Anneli Silvén Hagström, Andrea Reupert, Lotti O’Dea, Rose Cuff, Violette McGaw, Rochelle Hine

Bibliographic record

VenueSociology of Health & Illness · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsInjusticeMental healthPsychologyEpistemologySociologySocial psychologyPsychotherapistPhilosophy

Abstract

fetched live from OpenAlex

Amongst the impacts of growing up with a parent with mental health challenges is the experience of stigma-by-association, in which children and young people experience impacts of stigmatisation due to their parent's devalued identity. This article seeks to expand our understanding of this issue through an abductive analysis of qualitative data collected through a codesign process with young people. Results indicate that young people's experiences of stigmatisation can be effectively understood as experiences of epistemic injustice. Participants expressed that their experiences comprised 'more than' stigma, and their responses suggest the centrality to their experiences of being diminished and dismissed in respect of their capacity to provide accurate accounts of their experiences of marginalisation and distress. Importantly, this diminishment stems not only from their status as children, and as children of parents with mental health challenges but operates through a range of stigmatised identities and devalued statuses, including their own mental health status, sexual minoritisation, disability and social class. Forms of epistemic injustice thus play out across the social and institutional settings they engage with. The psychological and social impacts of this injustice are explored, and the implications for our understanding of stigma around family mental health discussed.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.049
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.349
Teacher spread0.310 · 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.

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

Citations15
Published2023
Admission routes1
Has abstractyes

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