How Can We Better Assist Caregivers With Understanding and Addressing the Cognitive Health Needs of People With Psychotic Disorders?
Bibliographic record
Abstract
Cognitive impairment is a prominent feature of psychosis-spectrum disorders that impedes functional recovery. Globally, clinical guidelines recommend that evidence-based treatments, including cognitive remediation and cognitive compensation, are offered to people with psychosis with cognitive impairment. Clinical guidelines also recommend that, where possible, family is involved in the mental health treatment of their loved ones more broadly. Nevertheless, there is little guidance on how to assist family members with understanding and addressing the cognitive health needs of people with psychotic disorders. This is despite a demonstrable relationship between this symptom domain and caregiver burden as well as a clear need for greater professional supports from the perspectives of consumers and carers. In this article, we highlight the impact of cognitive impairment in psychosis on caregiver outcomes and argue the need to increase efforts to promote knowledge about cognitive health among caregivers via cognition-specific psychoeducation and/or more active involvement in cognitive rehabilitation. We showcase some of the existing cognition-specific resources that are available to caregivers and propose areas in need of future research. We conclude this article by presenting several practical recommendations for how clinical teams can advance their support of family members caring for loved ones with psychosis and cognitive impairment when it is clinically appropriate to do so and the consumer their caregiving network are agreeable.
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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 | 0.017 | 0.063 |
| 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 | 0.004 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".