Exploring the Implementation of Cognitive Screening in First‐Episode Psychosis Settings: The <scp>CogScreen</scp> Implementation Study
Bibliographic record
Abstract
AIM: Accurate and appropriate cognitive screening can significantly enhance early psychosis care, yet no screening tools have been validated for the early psychosis population and little is known about current screening practices, experiences, or factors that may influence implementation. CogScreen is a hybrid type 1 study aiming to validate two promising screening tools with young people with first episode psychosis (primary aim) and to understand the context for implementing cognitive screening in early psychosis settings (secondary aim). This protocol outlines the implementation study, which aims to explore the current practices, acceptability, feasibility and determinants of cognitive screening in early psychosis settings from the perspective of key stakeholders. METHODS: Young people with first episode psychosis (n = 350), caregivers (minimum n = 10) and service providers (minimum n = 12) will be recruited from primary and specialist early psychosis services in Melbourne, Adelaide and Sydney, Australia. Two implementation science frameworks will inform data collection and analysis: the Theoretical Framework of Acceptability and the Consolidated Framework for Implementation Research. A mixed-methods design will be employed to collect and analyse data from questionnaires with young people, interviews with all stakeholder groups, and administrative processes. Quantitative data will be analysed using descriptive statistics. Qualitative data will be analysed through content analysis using deductive and inductive coding. RESULTS AND DISCUSSION: This protocol paper presents the rationale and methods for the CogScreen implementation study. CONCLUSION: Together with accuracy findings, results from the implementation study will provide insights about the practices, experiences, enablers and barriers to cognitive screening in early psychosis services.
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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 | 0.056 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| 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".