From intervention to evaluation: Assessing the feasibility of an exercise program for individuals with severe and persistent mental illness
Bibliographic record
Abstract
Mental illness in Canada carries a yearly economic burden of $51 billion. It is important to identify factors that can mitigate this burden by improving the health and well-being of individuals with severe and persistent mental illness (SPMI). Exercise may be one such factor. The purpose of this study was to examine the feasibility and effectiveness of a six-week tailored one-on-one exercise program for a local community mental health organization. Participants completed pre and post-intervention questionnaires to assess changes in mental health. Physical activity was assessed using both self-report and objective (accelerometers) measures. All quantitative measures are used for evaluation of the feasibility of the program. Interviews with the participants, key organizational stakeholders, and the trainers were also conducted. Overall, study retention and compliance with the study protocol was excellent. Participants (N=5) reported increases in physical activity levels and also improvements in additional lifestyle behaviours such as diet and substance use. Positive changes to mood were noted and perceptions of confidence increased. Participants reported desires for leadership roles in starting physical activity programs in their community homes and as ambassadors for health for people with SPMI. Interviews with the key organizational stakeholder and trainers provide support for the feasibility of the exercise program. As mental illness continues to strain the healthcare system, it is imperative that treatments other than pharmacology and psychotherapy are explored. Exercise may offer positive effects on the health of individuals with mental illness and ultimately reduce the burden of mental illness.
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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 | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".