Weight-Inclusive Physical Activity: A Systematic Evaluation of Virtual Resources
Bibliographic record
Abstract
BACKGROUND: Higher-weight individuals report lower rates of physical activity behavior and poorer physical activity experiences compared with their normative-weight counterparts, likely owing to the pervasiveness of weight stigma in physical activity contexts. Employing weight-inclusive strategies may improve physical activity outcomes, though little is known about the practical application of weight-inclusive principles in physical activity contexts. Furthermore, given the prominence of virtual methods of information dissemination, exploring online weight-inclusive resources is valuable. METHODS: Using Google, Instagram, and snowball searches, a virtual environmental scan was conducted to collect publicly available weight-inclusive physical activity resources. Two independent coders applied an a priori codebook to all eligible resources to evaluate the application of weight-inclusive principles. RESULTS: N = 80 weight-inclusive physical activity resources were identified, offering a range of educational materials (40%) and/or provision of physical activity services (76.3%). Virtual resources generally adhered to weight-inclusive principles by showcasing diversity in body size, using weight-inclusive language, and centering physical activity that honors the body's signals and cues; however, some also included weight-normative content. Provisional physical activity resources primarily targeted diverse-bodied end users, offered a range of physical activity types (eg, yoga, weight training, and dance), were membership-based, and offered asynchronous access. CONCLUSIONS: This study utilizes a systematic approach to collect and evaluate virtual, publicly available, and weight-inclusive physical activity resources. Virtual physical activity resources that adhere to weight-inclusive principles may be important for increasing accessible physical activity opportunities for higher-weight individuals.
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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 | 0.052 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| 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".