Methods for Researching the Physical Activity Impacts of ‘Natural Experiments’ in Modifying the Built Environment
Bibliographic record
Abstract
Modifying the built environment is increasingly recommended as a means of increasing physical activity, but there is currently little evidence from intervention studies to support this approach. From a discussion of 3 natural experiments in this area (RESIDE, iConnect, and Commuting and Health in Cambridge), several common lessons emerged. First, researchers should anticipate delays in the implementation of interventions that are outside their control, and research funders need to exercise a degree of flexibility to accommodate changing research timetables. Second, new built environments develop and evolve over time, and so do their effects on human behavior. Study designs and exposure measures should take account of this, and long term outcomes should be measured wherever possible to allow for potential sleeper, snowball, or threshold effects emerging over time. Third, it may be difficult to identify suitable control areas for a conventional parallel-group intervention–control design, and it may be necessary to draw on other study designs to provide a counterfactual comparison. Fourth, the effort and cost required to recruit, retain and obtain repeated measurements from participants over a period of years should not be underestimated. Finally, comprehensive process evaluation measures may be required to assess the level and quality of interventions.
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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 | 0.120 | 0.138 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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; 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".