Understanding and minimizing epidemiologic bias in public health research,” Canadian Journal of Public Health/Revue Canadienne de Sante’e Publique
Bibliographic record
Abstract
Awareness of potential biases is important for both researchers and policy-makers in public health: for researchers when designing and conducting studies, and for policy-makers when reading study reports and making decisions. This paper explains the meaning and importance of epidemiologic bias in public health and discusses how it arises and what can be done to minimize it. Examples of counting participants in a meeting, to which many policy-makers can relate, are used throughout the paper to illustrate bias in general, random error and systematic error, the effect of sample size, the three main categories of bias (selection, information and confounding), stratification and mathematical modeling. MeSH terms: Bias (epidemiology); epidemiology; selection bias; observer variation; confounding factors (epidemiology) Bias is explained in great detail inmany epidemiology textbooks1-9 andresearch papers10-14 but remains underappreciated. Often, bias is explained with detailed classification schemes,7,13
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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 | 0.359 | 0.441 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".