Exploring the Impact of the Occupational Therapy Health and Wellness Program (OT-HAWP) on Performance and the Health-Related Quality of Life of Cancer Survivors
Bibliographic record
Abstract
Creating innovative community-based programs for those living with and beyond cancer has the potential to improve outcomes; however, little has been done to explore these programs with participants that have various cancer diagnoses. We evaluated the impact of a 4-week community Occupational Therapy Health and Wellness Program (OT-HAWP) on self-perceived satisfaction and performance of daily activities, health-related quality of life, sleep quality, and fatigue among adults living with and beyond various cancer diagnoses. An uncontrolled, prospective, one-group pretest-posttest design was used. Participants completed patient reported measures of occupational performance and satisfaction (Canadian Occupational Performance Measure [COPM]), global health related quality of life (Patient-Reported Outcomes Measurement Information System-Global Health [PROMIS® Global Health]), sleep quality (Pittsburgh Sleep Quality Index [PSQI]), and the effect of fatigue on activities (Multidimensional Assessment of Fatigue [MAF]) before and after the program completion. Data was fully collected on 34 participants with various cancer diagnoses. For all outcomes, there was a statistically significant improvement after participating in the OT-HAWP program. Effect sizes range from small (0.46) to large (1.28). The OT-HAWP has the potential to improve self-reported occupational performance and satisfaction, global health-related quality of life, sleep quality, and fatigue in adults living with and beyond cancer in the community. Efficacy studies exploring community-based interventions are warranted to support legislation for improved access to care.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".