Employment, Studies and Feelings: Two to Nine Years After a Personalized Program of Cognitive Remediation in Psychiatric Patients
Bibliographic record
Abstract
Employment rate in psychiatry is around 10 to 30%. Cognitive remediation (CR) associated with psychosocial rehabilitation shows good functional outcomes, with a high level of satisfaction in participants provided by tailored CR. However, few studies looked at the long-term outcome in participants who experienced such a program. This retrospective survey examines the outcome of persons having psychiatric diseases 2 to 9 years after being treated with a personalized CR program. The survey included 12 domains with questions relevant to work, studies, before CR (T1) and at the moment of the survey (T2), questions about housing, relatedness, familiar relationships and daily activities at T2. Finally, a narrative interview was included to express feelings of the participants about CR. Sixty-six participants completed the survey, and were treated with neurocognitive or social cognition programs. Their diagnosis was: schizophrenia (80.3%), neurodevelopment disorder (autism as well as genetic or metabolic disease with psychiatric expression) (15.2%) and bipolar disorder (4.5%). The comparison between T1 and T2 showed significant difference for job employment (P < 0.001), even for competitive jobs (p < 0.007), for performing studies (p = 0.033), for practicing a physical activity (0.033) or reading (0.002). Outcome was also examined in reference to the delay from CR to highlight changes in patient characteristics and service delivery over the years. Hence, the total sample was split in two subgroups: CR delivered in 2009-2013 (n = 37); CR delivered in 2014-2016 (n = 29). While in the former group more participants were working (p = 0.037), in the latter group, which was younger (p = 0.04), more participants were studying (p = 0.02). At T2, a majority of persons experienced no relapse, three years (79.1%) to 8 years (56.8%) after CR, when referring to the anamnesis. Concerning subjective perception of CR, participants expressed feelings concerning positive impact on clarity of thought, on cognitive functions, self-confidence, perceiving CR as an efficient help for work and studies. To conclude, even long years after a personalized CR program, good benefits in terms of employment or studies emerge when compared to the status before CR, with good determinants for recovery in terms of leisure or physical activity practice.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".