Thinking About the Future of Cognitive Remediation Therapy Revisited: What Is Left to Solve Before Patients Have Access?
Bibliographic record
Abstract
In our previous paper on the Future of Cognitive Remediation published more than 10 years ago, we envisaged an imminent and wide implementation of cognitive remediation therapies into mental health services. This optimism was misplaced. Despite evidence of the benefits, costs, and savings of this intervention, access is still sparse. The therapy has made its way into some treatment guidance, but these documents weight the same evidence very differently, causing confusion, and do not consider barriers to implementation. This paper revisits our previous agenda and describes how some challenges were overcome but some remain. The scientific community, with its commitment to Open Science, has produced promising sets of empirical data to explore the mechanisms of treatment action. This same community needs to understand the specific and nonspecific effects of cognitive remediation if we are to provide a formulation-based approach that can be widely implemented. In the last 10 years we have learned that cognitive remediation is not "brain training" but is a holistic therapy that involves an active therapist providing motivation support, and who helps to mitigate the impact of cognitive difficulties through metacognition to develop awareness of cognitive approaches to problems. We conclude that, of course, more research is needed but, in addition and perhaps more importantly at this stage, we need more public and health professionals' understanding of the benefits of this therapy to inform and include this approach as part of treatment regimens.
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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 | 0.031 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.014 | 0.053 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.021 | 0.049 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".