The quick pivot: Capturing real world modifications for the re-implementation of an early psychosis program transitioning to virtual delivery
Bibliographic record
Abstract
Background: with limited guidance on how to deliver virtual clinical services or whether quality of re-implementation and treatment outcomes would be impacted. We used a structured approach to identify essential modifications for the delivery of core components and explored facilitators and barriers for re-implementation and fidelity of a virtually delivered EPI intervention. Materials and methods: NAVIGATE is a structured approach to team-based EPI. It provides detailed modules to guide delivery of core components including medication management, psychoeducation and psychotherapies, supported employment/education, and family education. Having initially implemented NAVIGATE at the Centre for Addiction and Mental Health (CAMH) in 2017, the EPI service transitioned to virtual delivery amid the COVID pandemic. Using a practice profile developed to support implementation, we detailed how core components of NAVIGATE were rapidly modified for virtual delivery as reported in structured group meetings with clinicians. The Framework for Reporting Adaptations and Modifications for Evidence-Based Interventions (FRAME) was used to describe modifications. Fidelity to the EPI standards of care was assessed by the First Episode Psychosis Fidelity Scale (FEPS-FS). Re-implementation barriers and facilitators and subsequent mitigation strategies were explored using structured clinician interviews guided by the Consolidated Framework for Implementation Research (CFIR). Results: Identified modifications related to the intervention process, context, and training. We identified contextual factors affecting the re-implementation of virtually delivered NAVIGATE and then documented mitigating strategies that addressed these barriers. Findings can inform the implementation of virtual EPI services elsewhere, including guidance on processes, training and technology, and approaches to providing care virtually. Discussion: This study identified modifications, impacts and mitigations to barriers emerging from rapid, unplanned virtual delivery of EPI services. These findings can support delivery of high-quality virtual services to youth with psychosis when virtual care is indicated.
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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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".