Processes of task performance as measured by the Assessment of Motor and Process Skills (AMPS): A predictor of work-related outcomes for adults with schizophrenia?
Bibliographic record
Abstract
OBJECTIVE: To determine whether the processes of task performance as measured by the Assessment of Motor and Process Skills (AMPS) would discriminate between the employment levels of adults with schizophrenia. PARTICIPANTS: Twenty adults with schizophrenia who were engaged either in competitive employment, supported employment, prevocational training, or non-vocational activities, participated in this exploratory study. METHODS: Each participant completed the AMPS, the Positive and Negative Syndrome Scale (PANSS), the Addiction Severity Index (ASI), and the Worker Role Interview (WRI) to gather data about their occupational performance, symptoms, drug / alcohol use, and psychosocial / environmental factors that might influence their work-related outcomes. RESULTS: Analysis revealed a moderate correlation between the level of employment and the global scores of the process skills scale in the AMPS. CONCLUSIONS: This should be seen as preliminary evidence that beyond the basic cognitive functions, processes of task performance may also be a predictor of work-related outcomes for this population. The results also highlighted the importance of considering personal causation and worker roles when assessing the work capacities of these clients. Finally, findings supported the four levels of employment used in this study, which appeared to form a continuum from non-vocational activities, prevocational training, supported employment, through to competitive employment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".