Use of relative and absolute effect measures in reporting health inequalities: structured review
Bibliographic record
Abstract
OBJECTIVE: To examine the frequency of reporting of absolute and relative effect measures in health inequalities research. DESIGN: Structured review of selected general medical and public health journals. DATA SOURCES: 344 articles published during 2009 in American Journal of Epidemiology, American Journal of Public Health, BMJ, Epidemiology, International Journal of Epidemiology, JAMA, Journal of Epidemiology and Community Health, The Lancet, The New England Journal of Medicine, and Social Science and Medicine. MAIN OUTCOME MEASURES: Frequency and proportion of studies reporting absolute effect measures, relative effect measures, or both in abstract and full text; availability of absolute risks in studies reporting only relative effect measures. RESULTS: 40% (138/344) of articles reported a measure of effect in the abstract; among these, 88% (122/138) reported only a relative measure, 9% (13/138) reported only an absolute measure, and 2% (3/138) reported both. 75% (258/344) of all articles reported only relative measures in the full text; among these, 46% (119/258) contained no information on absolute baseline risks that would facilitate calculation of absolute effect measures. 18% (61/344) of all articles reported only absolute measures in the full text, and 7% (25/344) reported both absolute and relative measures. These results were consistent across journals, exposures, and outcomes. CONCLUSIONS: Health inequalities are most commonly reported using only relative measures of effect, which may influence readers' judgments of the magnitude, direction, significance, and implications of reported health inequalities.
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 | 0.366 | 0.718 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.041 | 0.024 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".