Schizophrenia
Bibliographic record
Abstract
Jeff is a young man with schizophrenia who received occupational therapy (OT) during the acute stage and the day hospital/outpatient stage. This chapter describes the clinical practice of OT in Hong Kong and Singapore. Jeff and occupational therapists collaborated and discussed intervention plans. In the acute stage, the therapists conducted interviews related to the stress-vulnerability model and assessments using the Montreal Cognitive Assessment and Allen&s;s Cognitive Level Screen. The therapists collected information on Jeff&s;s stressors, protective factors, and cognition from these processes. Interventions in the acute stage focused on illness and stress management. During the day hospital or outpatient stage, the therapists adopted both the Model of Human Occupation and the stress-vulnerability model to conduct interviews and assessments using the Interest Checklist, Role Checklist, VALPAR Component Work Samples, MATRICS Consensus Cognitive Battery, etc. Interventions in this stage aimed to support Jeff&s;s engagement in his student, work, social, and leisure activities, as well as his illness and stress management. These interventions included individual sessions, involvement of peer support specialists, vocational training, Social Cognition and Interaction Training, cognitive training, leisure groups, caregiver groups, the Illness Management and Recovery program, etc. This case illustration shows the Oriental cultural aspects of OT.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.012 |
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".