Remote group therapies for cognitive health in schizophrenia-spectrum disorders: Feasible, acceptable, engaging
Bibliographic record
Abstract
Severe cognitive impairments and cognitive distortions are core to schizophrenia-spectrum disorders (SSDs) and are associated with deteriorated social functioning. Despite well-established efficacy of group psychosocial therapies targeting cognitive health in SSDs, dissemination of these programs remains limited. Remote delivery offers a promising strategy for increasing the programs' accessibility. Yet, little research has evaluated group therapies for cognitive health delivered in this way. Thus, we aimed to assess, from participants' and therapists' perspectives, the feasibility, acceptability, as well as levels and process of engagement in a videoconference delivery of group psychosocial therapies for SSD patients' cognitive health. Participants, outpatients, attended Action Based Cognitive Remediation or Metacognitive Training, both adapted for videoconference. Then, participants and therapists completed post-therapy questionnaires. Of the 28 participants attending at least one session, 75% completed more than half of sessions and seven dropped out. Technology did not appear to significantly hinder participation in the programs. All completing participants reported a positive experience with therapy, 67% were not bothered by the distance from the therapist, and 77% trusted that the information shared was kept confidential. Therapist-rated levels of attention M = 7.5/9 (SD = 1.04), participation M = 6.91/9 (SD = 1.32), and social interactions M = 5.31/9 (SD = 1.96) were satisfactory. Nonetheless, participants indicated that they would have appreciated more social interactions with group members. These positive results validate the earliest stage in the implementation process for remote group therapies targeting cognitive health in SSDs. Remote delivery promises to improve access to therapies targeting cognitive health and, ultimately, facilitate functional recovery for SSD patients.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".