Disseminating action‐based cognitive remediation to an early psychosis network: Clinician perspectives on feasibility and implementation barriers
Bibliographic record
Abstract
AIM: Action-based cognitive remediation (ABCR) is a group cognitive remediation treatment that aims to improve neurocognitive impairments experienced in patients with severe mental illness. Developed in research settings, ABCR is not yet widely available in community settings. As such, this study examines the feasibility of implementing ABCR in community clinics in an early psychosis network. METHODS: Eighty-five allied health professionals who work within an early psychosis intervention network were trained in the provision of ABCR. They were surveyed 6-months after training to gather information regarding their experience implementing ABCR within their clinical settings (e.g., barriers, perceived helpfulness of the treatment, modifications made to the manualized treatment). Access to ongoing training supports (e.g., treatment manual, asynchronous digital communication, conference calls) was also assessed. RESULTS: Fifty-one clinicians responded to the survey. Staff time, manager support, and equipment were rated as organizational barriers. Geographic location, other responsibilities, and motivation were rated as patient barriers. Over half of the sample modified the overall dose of ABCR to offer fewer sessions and/or shorter duration of sessions than the manualized approach. Clinicians that reduced the dose of ABCR reported significantly higher barriers with manager support than staff who delivered ABCR as manualized but did not report worse patient outcomes. We found asynchronous learning opportunities (i.e., manual, online discussion forum) were perceived as the most accessible and helpful methods of ongoing training support. CONCLUSIONS: The results provide preliminary information about barriers to implementing time-intensive cognitive treatments into clinical settings and may inform future training practices to increase successful implementation of cognitive remediation treatments.
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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 | 0.040 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".