Missing and accounted for: gaps and areas of wealth in the public health review literature
Bibliographic record
Abstract
BACKGROUND: High-quality review evidence is useful for informing and influencing public health policy and practice decisions. However, certain topic areas lack representation in terms of the quantity and quality of review literature available. The objectives of this paper are to identify the quantity, as well as quality, of review-level evidence available on the effectiveness of public health interventions for public health decision makers. METHODS: Searches conducted on http://www.health-evidence.ca produced an inventory of public health review literature in 21 topic areas. Gaps and areas of wealth in the review literature, as well as the proportion of reviews rated methodologically strong, moderate, or weak were identified. The top 10 topic areas of interest for registered users and visitors of http://www.health-evidence.ca were extracted from user profile data and Google Analytics. RESULTS: Registered users' top three interests included: 1) healthy communities, 2) chronic diseases, and 3) nutrition. The top three preferences for visitors included: 1) chronic diseases, 2) physical activity, and 3) addiction/substance use. All of the topic areas with many (301+) available reviews were of interest to registered users and/or visitors (mental health, physical activity, addiction/substance use, adolescent health, child health, nutrition, adult health, and chronic diseases). Conversely, the majority of registered users and/or visitors did not have preference for topic areas with few (≤ 150) available reviews (food safety and inspection, dental health, environmental health) with the exception of social determinants of health and healthy communities. Across registered users' and visitors' topic areas of preference, 80.2% of the reviews were of well-done methodological quality, with 43.5% of reviews having a strong quality rating and 36.7% a moderate review quality rating. CONCLUSIONS: In topic areas in which many reviews are available, higher level syntheses are needed to guide policy and practice. For other topic areas with few reviews, it is necessary to determine whether primary study evidence exists, or is needed, so that reviews can be conducted in the future. Considering that less than half of the reviews available on http://www.health-evidence.ca are of strong methodological quality, the quality of the review-level evidence needs to improve across the range of public health topic areas.
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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 | 0.260 | 0.681 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.056 | 0.061 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".