Grading of evidence of the effectiveness of health promotion interventions: Table 1
Bibliographic record
Abstract
AIMS: Grading of evidence of the effectiveness of health promotion interventions remains a priority to the practise of evidence-based health promotion. Several authors propose grading the strength of evidence based on a hierarchy: convincing, probable, possible and insufficient; or strong, moderate, limited and no evidence. Although these grading hierarchies provide simple and straightforward rankings, the terms that describe the categories in the hierarchies, however, do not explain, in an explicit manner, in what way the strength of the evidence in one category is more, or less, superior than that in another. METHODS: To enhance the explanatory power of the hierarchy, we propose that evidence be classified into three grades, each with a short explanatory note on the basis of three criteria: the degree of association between the intervention under study and the outcome factors, the consistency of the findings from different studies, and whether there is a known cause-effect mechanism for the intervention under study and the outcome factors. CONCLUSION: For more in-depth grading, a three-grade expanded hierarchy is also recommended. Examples are given to illustrate our proposed grading schemes.
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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 | 0.063 | 0.223 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.028 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".