Beyond symptom improvement: transdiagnostic and disorder‐specific ways to assess functional and quality of life outcomes across mental disorders in adults
Bibliographic record
Abstract
Improving meaningful outcomes is the main goal of clinical care for mental disorders. Traditionally, the focus in clinical research and practice has been on outcome domains that refer to symptom severity or service use (e.g., hospitalization), relate to categorical diagnoses, and favour clinician-rated measures. More recently, self-rated and dimensional as well as transdiagnostic outcome domains have gained traction, and functioning, quality of life and well-being/life satisfaction, along with the construct of personal recovery, have become a stronger focus. These key multidimensional outcome domains need to be properly defined and assessed. Further, the concepts of "functional" and "personal" recovery need to be differentiated. "Functional recovery" is defined by observed functioning across the domains of self-care, social interactions, leisure time activities, and educational or vocational activities. "Personal recovery" involves the subjective sense of living a personally meaningful life, irrespective of whether symptoms continue, or ongoing/intermittent support is needed. Despite the multi-stakeholder relevance of these outcome domains, no comprehensive account of how to measure them is available. To fill this gap, we provide here an overview of the main tools to assess functioning, quality of life/well-being/life satisfaction, and personal recovery outcomes across mental disorders in adults, aiming to also identify additional needs that should be addressed. We identified tools that can be used in clinical and research practice to assess people with the following mental health conditions: anxiety disorders, bipolar disorder, dementias, eating disorders, major depressive disorder, obsessive-compulsive and related disorders, personality disorders, post-traumatic stress disorder, schizophrenia, and substance use disorders. Both transdiagnostic and disorder-specific measures are described. Suggested tools were selected keeping feasibility and scalability needs in mind. The incorporation of these measures in both research and clinical care will enrich patient assessment as well as treatment planning and evaluation, increasing the likelihood of enhanced outcomes in people living with mental disorders.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".