Will web-based research suffice when collecting U.S. school district policies? The case of physical education and school-based nutrition policies
Bibliographic record
Abstract
BACKGROUND: Recognizing the growing childhood overweight problem, a number of school-based strategies, including policy approaches, have been proposed and are being implemented to address the problem considering the amount of time children spend in schools. This paper describes the results of a pilot study that tested approaches to collecting U.S. school district policy information regarding physical education and nutrition requirements that can inform efforts by policy makers, researchers, advocates and others interested in collecting school district-level obesity-related policies that are typically not systematically available from a "one stop" source. METHODS: Sixty local school districts representing six states were selected for conducting the district policy research, with larger, urban school districts over-sampled to facilitate collection of policies from districts representing a larger proportion of the public school population in each study state. The six states within which the pilot districts were located were chosen based on the variability in their physical education and school-based nutrition policy and geographic and demographic diversity. Web research and a mail canvass of the study districts was conducted between January and May 2006 to obtain all relevant policies. An additional field collection effort was conducted in a sample of districts located in three study states to test the extent to which field collection would yield additional information. RESULTS: Policies were obtained from 40 (67%) of the 60 districts, with policies retrieved via both Web and mail canvass methods in 16 (27%) of the districts, and were confirmed to not exist in 10 (17%) of the districts. Policies were more likely to be retrieved from larger, urban districts, whereas the smallest districts had no policies available on the Web. In no instances were exactly the same policies retrieved from the two sources. Physical education policies were slightly more prevalent than nutrition policies. CONCLUSION: Collection of U.S. local school district policies requires a multi-pronged approach. Web research and mail canvasses will likely yield different types of policy information. Given the variance in district-level Web site presence, researchers and others interested in obtaining district physical education and nutrition-related policies should consider supplementing Web research with more direct methods.
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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 | 0.216 | 0.338 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".