Narratives in context: a cellphilm study of the social experiences of persons with psychosis from different ethnic, racial and migrant backgrounds
Bibliographic record
Abstract
BACKGROUND: A higher risk of psychosis among migrants and ethnic minorities, due to intersecting exposure to social disadvantage, exclusion and discrimination, has been reported. However, first-person experiences and perspectives regarding these topics have rarely been sought. METHODS: We aimed to explore the contexts, experiences, and perspectives of individuals with psychosis from diverse ethno-racial and migrant backgrounds through a qualitative study involving an in-depth interview and an arts-based component (cellphilming). RESULTS: Four themes were generated through thematic analysis: Facing adversity; Apart from the world; (Re)building structure; and meaning; and Cellphilming as possibility and connection. Themes portray the role of place and society in the lives and development of psychosis of participants. CONCLUSIONS: Findings resonate with previous research on the impacts of social and structural disadvantage, particularly for minoritized populations. By framing these under particular contexts and life stories, our findings allow for contextualization and nuance, and a focus on what mattered the most for participants: hope, meaning, renewal and healing.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".