Daily Time Use as a Measure of Community Adjustment for Persons Served By Assertive Community Treatment Teams
Bibliographic record
Abstract
PURPOSE: The purpose of the study was to examine daily time use of clients of Assertive Community Treatment (ACT) as a measure of their community adjustment and well-being. The actual daily time use of ACT clients in the four categories of personal care, productivity, leisure, and sleep were compared to the data for Canadian population norms. METHOD: Daily time use data were collected from 27 adult clients from two Assertive Community Treatment Teams in southeastern Ontario using recall time diaries of two weekdays. The data were coded using the Statistics Canada (1999) coding scheme. Descriptive statistics were used to determine time in the major categories of time use and z scores were used to compare the study sample to the adult Canadian population. The percentages of time spent in specific subcategories of activity were also compared. RESULTS: The results indicated an imbalance in occupation with time use dominated by leisure and sleep activities. Study participants spent significantly more time in passive leisure compared to active leisure and socialization. CONCLUSION: The activity patterns of ACT clients were not consistent with those associated with community adjustment, health, and well-being. Occupational therapists working in ACT are in a good position to contribute to the literature regarding occupational performance and mental illness and to lead ACT teams in discussions and practices that may promote health through activity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".