Printed in U.S.A. Place Effects for Areas Defined by Administrative Boundaries
Bibliographic record
Abstract
This study estimates the effects of place on the distribution of health problems, health-related quality of life, general well-being, and family functioning for youths and adults aged 12 years and older. Data come from the Ontario Health Survey, a cross-sectional study done in 1990 to provide baseline statistical data on population health within 42 public health units throughout the province. Place effects were generally small and were influenced by both the size of the geographic area used to define place and the health indicator selected for study. Variations in health explainable at the public health region level were less than 1%. Variations in health explainable within smaller geographic boundaries (enumeration areas) ranged from 4.7 % for health problems to 0.2 % for family functioning. Adjustment for area differences in the age, gender, education, marital status, income, and birthplace of inhabitants reduced these place effects at the enumeration area level to 3.7 % for health problems and to less than 0.1 % for family functioning. The lack of evidence for place effects within large jurisdictional boundaries raises questions about both the usefulness of carrying out health needs assessment surveys within these areas and the informativeness of these geographic boundaries for studying place effects. Am J Epidemiol 1999; 149: 577-85. epidemiologic methods; health; small-area analysis A number of disciplines have had a long-standing
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.444 | 0.156 |
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".