Participant Bias in Community-Based Physical Activity Research: A Consistent Limitation?
Bibliographic record
Abstract
Physical activity is a beneficial, yet complex, health behavior. To ensure more people experience the benefits of physical activity, we develop and test interventions to promote physical activity and its associated benefits. Nevertheless, we continue to see certain groups of people who choose not to, or are unable to, take part in research, resulting in "recruitment bias." In fact, we (and others) are seemingly missing large segments of people and are doing little to promote physical activity research to equity-deserving populations. So, how can we better address recruitment bias in the physical activity research we conduct? Based on our experience, we have identified 5 broad, interrelated, and applicable strategies to enhance recruitment and engagement within physical activity interventions: (1) gain trust, (2) increase community support and participation, (3) consider alternative approaches and designs, (4) rethink recruitment strategies, and (5) incentivize participants. While we recognize there is still a long way to go, and there are broader community and societal issues underlying recruitment to research, we hope this commentary prompts researchers to consider what they can do to try to address the ever-present limitation of "recruitment bias" and support greater participation among equity-deserving groups.
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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 | 0.632 | 0.758 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.011 | 0.038 |
| Scholarly communication | 0.016 | 0.028 |
| Open science | 0.014 | 0.011 |
| Research integrity | 0.030 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".