In the waiting line: a narrative analysis of patients and caregivers waiting for early intervention psychosis services in Canada
Bibliographic record
Abstract
Introduction Our patients often come to us through wait-lists, but when does the wait really start? Early intervention psychosis programs have introduced policies and benchmarks aimed at minimizing the time between referral and first contact. While programs that meet these targets are often celebrated for their success in reducing untreated psychosis, these targets leave out a critical piece of the puzzle: the time elapsed from symptom onset. Understanding how patients and caregivers experience the wait for care in its entirety is critical to further reducing treatment delays in psychosis. Objectives This presentation aims to first examine a disconnect between early intervention wait-list policies and patients’ experiences. Next, it will explore how patient and family member narratives reflect the complex and nuanced nature of waiting for care. Finally, it will propose clinically relevant solutions for reducing the delay between symptom onset and appropriate treatment. Methods We conducted individual semi-structured interviews with patients and caregivers accessing early intervention psychosis services across Canada. For this presentation, three interviews from different geographic and socio-cultural regions were selected for their distinct perspectives. We performed a two-reviewer narrative inquiry to derive emergent narratives about waiting for services. Results Patient and caregiver experiences revealed two distinct waiting periods. Aside from the “wait-list” period between referral and first contact that is addressed by early intervention policies, participants noted experiencing a much longer initial waiting period, with narratives beginning at symptom onset. Participants described this period as an active, dynamic, frustrating, and often traumatic process that involves multiple ER visits and attempts at receiving care. Conclusions We propose formally distinguishing between two forms of waiting for services: passive waiting, which is the state of being on a wait-list, and active waiting, which begins at symptom onset and includes the complex struggle to receive stable care. Early intervention programs’ efforts to reduce passive waiting are important, but the high burden of active waiting suggests a need for larger efforts such as clinician education and systemic changes in how patients access healthcare. Reducing active wait times could truly transform how first episode psychosis is managed and improve outcomes for those in urgent need. Disclosure of Interest None Declared
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".