Structural Disadvantage and Culture, Race, and Ethnicity in Early Psychosis Services: International Provider Survey
Bibliographic record
Abstract
OBJECTIVE: Little is known about provider perspectives on programmatic responses to structural disadvantage and cultural differences within early intervention in psychosis (EIP) services, programs, and models. The primary objective of this study was to investigate providers' perspectives on the impacts of disadvantage and minority race, ethnicity, and culture and to describe current practices and perceived gaps and concerns. METHODS: An online survey of specialized EIP providers was disseminated in the United Kingdom, United States, Canada, Australia, and Chile. A total of 164 providers, representing 110 unique sites, completed the survey. Closed-ended questions gathered demographic and program data, including information on formal assessment of trauma or adversity, integration of trauma-informed care, integration of formal cultural assessment tools, training focused on culture, programmatic changes to address culture-related issues, and consultation with cultural insiders. Open-ended questions addressed the demographic mix of the program's client population; the perceived role and influence of trauma, structural disadvantage, and cultural differences; and concerns and needs related to these topics. Frequencies were examined for closed-ended items; open-ended responses were systematically coded. RESULTS: Overall, survey findings suggested low levels of implementation of a variety of assessment and support practices related to cultural diversity in EIP programs. Coding of open-ended responses revealed numerous concerns regarding the impacts of disadvantage and cultural difference on clients and perceived gaps in policy and implementation. CONCLUSIONS: An expansion of research and service development aimed at better meeting the disadvantage- and culture-related needs of young people with early psychosis and their families should be a priority for the field.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".