Physical Activity and Mental Health in Children and Youth: Clinician Perspectives and Practices
Bibliographic record
Abstract
Abstract Background Rates of mental health challenges among children and youth are on the rise. Physical activity has been identified as a promising intervention to improve mental health outcomes for youth. Objective This study aimed to investigate how mental health clinicians perceive and utilize physical activity as a mental health intervention for children and adolescents. Methods Seventy-four Ontario mental health clinicians (psychologists, psychotherapists, and social workers) were surveyed about their perspectives and practices related to physical activity as part of mental health care for children and adolescents using a mixed-methods approach. Survey respondents were also asked about barriers to including physical activity in care. Results Although 100% of clinicians agreed that physical activity was beneficial to their own and to their clients’ mental health, only 61% reported prescribing physical activity to their clients. Barriers to prescribing physical activity as a treatment option included lack of training, time, and resources. Clinicians who were more physically active themselves were more likely to view physical activity as beneficial for mental health and were less likely to view time as a barrier to discussing and prescribing physical activity in their practice. Many clinicians expressed a need for more training, knowledge, and resources. Conclusions The results of this study suggest that with improved access to training and resources, clinicians will be well-positioned to leverage the benefits of physical activity for mental health in their practice.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".