Validation of Secondary Commercial Data Sources for Physical Activity Facilities in Urban and Nonurban Settings
Bibliographic record
Abstract
BACKGROUND: Secondary data are often necessary to assess the availability of commercial physical activity (PA) facilities and examine its association with individual behaviors and outcomes, yet the validity of such sources has been explored only in a limited number of studies. METHODS: Field data were collected on the presence and attributes of commercial PA facilities in a random sample of 30 urban, 15 suburban, and 15 rural Census tracts in the Chicago metropolitan statistical area and surrounding area. RESULTS: Approximately 40% of PA establishments in the field data were listed for both urban and nonurban tracts in both lists except for nonurban tracts in D&B (35%), which was significantly improved in the combined list of D&B and InfoUSA. Approximately one-quarter of the PA facilities listed in D&B were found on the ground, whereas 40% to 50% of PA facilities listed in InfoUSA were found on the ground. PA establishments that offered instruction programs or lessons or that had a court or pool were less likely to be listed, particularly in the nonurban tracts. CONCLUSIONS: Secondary commercial business lists on PA facilities should be used with caution in assessing the built environment.
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 | 0.066 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".