Bibliographic record
Abstract
evidence-health-patient-choice-global-policy.As a resource for health practitioners and policy makers, Anne Andermann's Evidence for Health offers a comprehensive guide to evidence-informed decision making at both personal and public levels.In their astute 2009 Lancet article, Koplan et al. [1] defined global health as ''an area for study, research, and practice that places a priority on improving health and achieving equity in health for all people worldwide' ' (p.1995).Evidence for Health is a highly accessible and practical guide for knowledge translation as it relates to global health policy creation and interpretation.The content of the book includes factors affecting the ways in which health is conceptualized and on a more micro level, the steps involved in finding valid and relevant information in decision making as it relates to personal health.
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 | 0.110 | 0.262 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.038 |
| Scholarly communication | 0.019 | 0.040 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".