Epistemic injustice in experiences of young people with parents with mental health challenges
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".