Impact of Occupation-Based Groups on Occupational Performance and Satisfaction Outcomes: Pilot Study
Bibliographic record
Abstract
Occupation-based groups can be used to improve occupational performance outcomes in the inpatient rehabilitation setting. It remains unclear whether they offer comparable outcomes to occupation-based interventions delivered individually. This study aims to pilot an occupation-based group intervention and compare occupational performance, satisfaction, and goal attainment outcomes with usual care. Twenty-one participants (15 women, 6 men, aged 34–85) were allocated to control ( n = 11) and intervention ( n = 10) groups. The control group received usual care (individual occupation-based interventions), while the intervention group received usual care plus an occupation-based group intervention. The method used a pilot quasi-experimental pre- to post-intervention design with a nonequivalent control group. The primary outcome measures were the Canadian Occupational Performance Measure (COPM) and the Goal Attainment Scale (GAS). No significant between-group differences were found; both groups reported statistically significant improvements with medium to large effect sizes. Pilot data suggests that occupation-based groups offered comparable outcomes to individual treatment; a larger sample size is required to draw conclusions on their impact. Australian New Zealand Clinical Trials Registry ( https://uat.anzctr.org.au/Default.aspx ) was accessed on November 20, 2023. Registration number: ACTRN12623001196639.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".