Physical Activity in Mental Health Treatment: Clinician Perspectives and Practices
Bibliographic record
Abstract
Background/Objectives: The beneficial effects of physical activity on mental health and well-being are well established. The integration of physical activity into psychotherapeutic treatment for mental health difficulty holds promise as an avenue to reduce symptoms and support well-being. Mental health clinicians have previously indicated an interest in the use of physical activity in treatment, but it is unclear to what extent physical activity interventions are implemented in clinical mental health care. The present study aimed to understand mental health clinicians’ practices related to physical activity, as well as to investigate their related training and knowledge. Methods: Semi-structured interviews were conducted with mental health clinicians, including registered psychologists, psychotherapists, and social workers. Inductive content analysis was performed to identify key themes related to practices, training experiences, and training interests. Results: Clinicians reported making recommendations for physical activity and using a range of in-session strategies to include physical activity in mental health treatment. Clinicians reported that their knowledge and training about physical activity was obtained primarily from informal sources. Clinicians indicated an interest in further training, with an emphasis on practical strategies. Conclusions: Mental health clinicians demonstrated an interest in the use of physical activity as part of psychotherapeutic treatment. Some clinicians routinely integrate physical activity into treatment, while others express a need for further training in this area.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".