Challenges in assessing the implementation and effectiveness of physical activity and nutrition policy interventions as natural experiments
Bibliographic record
Abstract
The Ontario (Canada) government has instituted a policy requiring elementary schools to offer at least 20 min of daily physical activity for students in Grades 1-8 and replace non-nutritious vending machine foods with healthier choices. These policy interventions represent 'natural experiments' offering unique opportunities for conducting research and evaluation. The use of natural experiments to contribute evidence on the effectiveness of policy interventions is identified as an underused tool for public health [Tudor-Locke, C., Ainsworth, B. E. and Popkin, B. M. (2001) Active commuting to school: an overlooked source of children's physical activity? Sports Medicine, 31, 309-313; Petticrew, M., Cummins, S., Ferrell, C., Findlay, A., Higgins, C., Hoy, C. et al. (2005) Natural experiments: an underused tool for public health? Public Health, 119, 751-757]. To date, some Canadian school-based food and nutrition policies are being monitored, but their impact on child and youth obesity is unknown [Canadian Institute for Health Information. (2006) Improving the Health of Canadians: Promoting Healthy Weights, Ottawa, ON]. There are a number of challenges to the evaluation of policy interventions as natural experiments. Often, there are little or no baseline data available to use as the basis for assessing change. Government policies that result in the adoption of particular approaches across large jurisdictions, such as provinces, may result in wide variation in the design and implementation of interventions. Thus, in some cases, natural experiments may be at risk of having low potential to be adequately evaluated on key outcomes. In this paper, we discuss the context of these challenges in relation to the Ontario government school physical activity and nutrition policies.
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 | 0.781 | 0.847 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.016 | 0.013 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".