International law’s effects on health and its social determinants: protocol for a systematic review, meta-analysis, and meta-regression analysis
Bibliographic record
Abstract
BACKGROUND: In recent years, there have been numerous calls for global institutions to develop and enforce new international laws. International laws are, however, often blunt instruments with many uncertain benefits, costs, risks of harm, and trade-offs. Thus, they are probably not always appropriate solutions to global health challenges. Given these uncertainties and international law's potential importance for improving global health, the paucity of synthesized evidence addressing whether international laws achieve their intended effects or whether they are superior in comparison to other approaches is problematic. METHODS: Ten electronic bibliographic databases were searched using predefined search strategies, including MEDLINE, Global Health, CINAHL, Applied Social Sciences Index and Abstracts, Dissertations and Theses, International Bibliography of Social Sciences, International Political Science Abstracts, Social Sciences Abstracts, Social Sciences Citation Index, PAIS International, and Worldwide Political Science Abstracts. Two reviewers will independently screen titles and abstracts using predefined inclusion criteria. Pairs of reviewers will then independently screen the full-text of articles for inclusion using predefined inclusion criteria and then independently extract data and assess risk of bias for included studies. Where feasible, results will be pooled through subgroup analyses, meta-analyses, and meta-regression techniques. DISCUSSION: The findings of this review will contribute to a better understanding of the expected benefits and possible harms of using international law to address different kinds of problems, thereby providing important evidence-informed guidance on when and how it can be effectively introduced and implemented by countries and global institutions. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42015019830.
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 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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".