Statistical method use in public health research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIMS: The content of public health research is often statistically complex. This review seeks to assess the breadth of statistical literacy required to understand this material, with a view to informing practitioners' statistical training. METHODS: We review the statistical content of original research articles published in 2011 in four major public health journals. Categories of statistical methodologies are identified and their frequency of use recorded. Methods' "usefulness" in terms of the extent to which their understanding increases accessibility to the literature is assessed. RESULTS: A total of 482 articles were reviewed and 30 categories of methods identified. Along with descriptive statistics (467 articles), regression analyses were also common, with logistic regression (206 articles) more than twice as prevalent as linear regression (95 articles). More complex regression models for use with clustered data were also commonly encountered, appearing in 96 articles. CONCLUSIONS: The public health literature features a wide variety of statistical methods, some of which are advanced. To ensure the literature remains accessible, training for public health practitioners should include statistical training that maximizes breadth as well as depth of understanding.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.152 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it