Health Benefits of Physical Activity: A Strengths-Based Approach
Bibliographic record
Abstract
Our special series on Cardiac Rehabilitation outlined the importance of routine physical activity and/or exercise participation in the primary and secondary prevention of cardiovascular disease and many other chronic medical conditions. The evidence is overwhelming, demonstrating that nearly everyone can benefit from becoming more physically active. This messaging has been widely disseminated at regional, national, and international levels. Often, this messaging highlights a physical inactivity crisis and the health perils of not engaging in sufficient amounts of physical activity. This deficits-based messaging often includes generic threshold-based recommendations stating that health benefits can only be accrued with specific volumes or intensities of physical activity. In this Editorial, we argue that the current generic and deficits-based messaging misses a great opportunity to focus on the positive and to facilitate hope and real change at the individual, community, and population levels. We advocate a strengths-based approach to health and wellness promotion that focuses on the innate strengths of individuals, families, and communities to enable self-empowerment and self-determination related to health and wellness. By taking a strengths-based approach, we can build hope, promoting the positive aspects of routine physical activity and exercise participation and providing a greater opportunity to enhance health and wellbeing for everyone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.031 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".