Referral pathways to early intervention services for psychosis and their influence on perceptions of care: an Interpretive phenomenological analysis
Bibliographic record
Abstract
Background: Most young adults experiencing psychosis enter early intervention services for psychosis (EIS) via inpatient services and emergency departments. Referral in this manner is believed to increase young adults’ risk for adverse outcomes while in EIS such as diminished trust and engagement in services. However, little research has empirically examined the impact of young adults’ referral and help-seeking experiences on their subsequent views towards EIS and engagement to services. Methods: This qualitative study involved semi-structured interviews with 12 young adults (mean age = 24.83) within 3-12 months following acceptance to an EIS. Interviews aimed to examine their experiences of help-seeking and referral to EIS as well as the impact of these experiences on their subsequent perception of, and engagement with EIS. Data were analyzed using the Interpretative Phenomenological Analysis approach. Results: 3 superordinate themes emerged: (1) Navigating the Maze of Healthcare (2) Dignity and (3) Impact of Events . Participants with referral pathways involving urgent care services, particularly through involuntary hospitalization, described more adversity during their referral pathway and were more likely to describe help-seeking experiences as contributing to negative views towards EIS and diminished engagement in treatment. Conclusions: The impact of early negative experiences with healthcare on views towards EIS and engagement is evident in participants’ accounts. Sense making was further contextualized by participants’ illness insight, degree of recovery, and social support throughout experiences. Emergent themes highlight the need for psychiatric services to emphasize service users’ dignity and for EIS to provide opportunities for patients to process past negative healthcare experiences to strengthen engagement.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".