Developing physical activity interventions for individuals with schizophrenia
Bibliographic record
Abstract
Schizophrenia is the most disabling and persistent form of severe mental illness (SMI). Life expectancy is shorter by 15 years primarily because of coronary artery disease. Research is urgently required in developing evidence-based behavioural interventions for preventing and treating obesity and diabetes that are specific to this population. In particular, reducing the high prevalence of physical inactivity is a priority. This presentation will provide an overview of a research programme of a sequential series of phases leading to the creation and piloting of two interventions that promote active lifestyles including an individual level, modified form of exercise counselling and a group-mediated cognitive behavioural intervention. These studies have demonstrated mixed success in changing key psychological mediators and physical activity as measured by accelerometry or self-report. The development and ongoing implementation of this work will be discussed in light of a number of systemic barriers to physical activity promotion within mental health settings. These range from the nature of the illness as being one characterized by amotivation, to the increasing shift to ‘care in the community’ models of practice, and reductions in the number of specialized professionals who could play a role in promoting physical activity. Overall, this phased pilot work suggests multi-level ecological interventions are feasible and acceptable to individuals with schizophrenia, and that modest benefits can be attained through intervention. Developing sustainable forms of intervention that can be delivered in community settings remains a future challenge. Kinesiologists may have an important role to play as extended members of community mental health teams. Acknowledgments: Work reported in this abstract was supported by the Canadian Institutes of Health Research and the Ontario Mental Health Foundation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".