Identifying and Assessing Barriers to Optimal Outcomes for Psychosis: An Important Research Focus
Bibliographic record
Abstract
Schizophrenia and related psychotic disorders can have significant impact, including direct and indirect economic costs, on the person, their circle of support, and society. While we must pursue research to allow for an understanding of the biological underpinnings of psychosis, there also remains a need for continued, consistent, and persistent evaluation of what is required to improve outcomes for people at all phases of illness. The optimal outcome goal is recovery, an entity that encompasses not only symptomatic control but also elements of independent functioning, requiring no or minimal support and a personal sense of well-being. A cornerstone to this end is sustained remission, which includes control of relapses and hospitalizations. Four papers1–4 in this edition of The Canadian Journal of Psychiatry contribute to what is imperative: to identify and to assess barriers to optimal outcomes. Long-term trajectories of symptomatic and functional outcomes are often established in the first 2 to 5 years of a psychotic illness, and represent the rationale behind the development of phase-appropriate treatment in early intervention services (EISs) for psychosis. A critical consideration is that if we are, indeed, passionate about optimizing a person’s outcomes, then we must be able to fully engage that person in treatment so that they can benefit from services. Therefore, understanding the elements that define service engagement and disengagement in EISs for psychosis is critical, and deserving of our attention and research. Dr Shalini Lal and Dr Ashok Malla1 offer a thoughtful perspective on this topic. They discuss how service disengagement is a real issue, with about 30% of young adults disengaging from EISs. They examine the more accessible literature on a person’s liabilities for disengagement (for example, insight and substance use), but additionally develop a multi-dimensional framework for future research that also considers liabilities associated with the service provider, the EIS program, and mental health system components (for example, types of interventions and integration of services). Importantly, they lay the groundwork for more meaningful discussions and research directions that incorporate qualitative and quantitative research with the youth and young adult, their families, and service providers. The authors underscore the importance of understanding the experiences and perspectives of the patient, families, and service providers as they relate to service engagement. This broader knowledge base, they correctly argue, can help inform definitions and measurements of engagement, and inform clinical practice so that by minimizing disengagement, maximal EIS benefits are realized. In furthering the examination of determinants of outcomes in early phase psychosis, Dr Christy L M Hui et al2 examined paternal age (at birth) and other more common determinants as they relate to relapse risk in the first year of illness. They report on a sample out of Hong Kong that advanced paternal age modestly but significantly increased the risk of relapse, and importantly, this finding was independent of factors such as medication adherence, family history, duration of untreated psychosis, and maternal age. When stratifying the age groups, this effect on relapse was the strongest in patients whose fathers were over the age of 40 at birth. This is considered a nonmodifiable risk factor when conceptualized biologically—a potentially important and easily identified factor that could aid in identification of a subgroup of EIS patients with psychosis at high risk of relapse who would then benefit with more directed intensive relapse prevention interventions. Interestingly, benefits of subgroup identification also holds when paternal age is viewed from a psychosocial viewpoint, that being the potential for older fathers to be identified for more directed psychoeducation to ensure caregiver risk of relapse is minimized. While it is recognized that sleep disturbances can be a relapse risk indicator (part of a person’s relapse signature) and that they can affect daytime functioning in people with psychosis, Dr Bryony Sheaves et al3 have explored the link between nightmares in a psychotic population in south London with sleep and daytime impairment. In a convenience sample of 40 people with psychosis, using standardized scales, they reported that 55% experienced regular nightmares, significantly more than what is reported in the general population. Additionally, they reported a link between nightmare frequency and overall sleep quality and efficiency, as well as nightmare distress to measures of daytime functioning. The nightmare literature in posttraumatic stress disorder may help to further clarify the role of nightmares in psychosis psychopathology (for example, nightmares’ role in delusion maintenance) as well as possible avenues to explore regarding treatment. Discussing sleep variables in any great degree with people with psychosis is not common practice; this avenue of research may indicate our reconsideration of its importance. Finally, health administrative data can be used to assess if interventions in engagement, risk identification, and sleep, as well as other system-level interventions for psychosis, have an effect on health care service use (thus on health outcomes). Dr Paul Kurdyak et al4 tested if a valid, population-based sample with chronic psychosis within a large population-based administrative database in Ontario could be generated. Important in this paper is the discussion around the merits and limitations of the various algorithms for data extraction—considerations depending on the population to be studied (for example, comparing early phase psychosis with more severely ill, late-phase psychosis). Validating these algorithms is a critical exercise in our goal of assessing the success of interventions on outcomes in psychosis. Engagement, relapse risk indicators, and valid mechanisms to investigated outcome interventions in psychotic populations are all important avenues for research. Understanding these, and other barriers and facilitators, will allow us to continue to move toward recovery in psychosis; moving away from the diagnostic nihilism that can exist in both the clinical and general populations.
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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 | 0.113 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".