Jump step - a community based participatory approach to physical activity & mental wellness
Bibliographic record
Abstract
BACKGROUND: There is a physical inactivity pandemic around the world despite the known benefits of engaging in physical activity. This is true for individuals who would receive notable benefits from physical activity, in particular those with mood disorders. In this study, we explored the factors that facilitate and impede engagement in physical activity for individuals with a mood disorder. The intent was to understand the key features of a community based physical activity program for these individuals. METHODS: We recruited and interviewed 24 participants older than 18 with Major Depressive Disorder or Bipolar II. The interviews were conducted by peer researchers. The interviews were transcribed and analyzed using NVivo 10™. Thematic analysis was used to analyze the data. RESULTS: The facilitators to physical activity include being socially connected with family and friends, building a routine in daily life, and exposure to nature. The barriers to physical activity include the inability to build a routine owing to a mood disorder, and high cost. The ideal exercise program comprises a variety of light-to-moderate activities, offers the opportunity to connect with other participants with a mood disorder, and brings participants to nature. The average age of our participants was 52 which could have influenced the preferred level of intensity. CONCLUSION: The individuals in this study felt that the key features of a physical activity program for individuals with a mood disorder must utilize a social network approach, take into account the preferences of potential participants, and incorporate nature (both green and blue spaces) as a health promotion resource.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".