Mortality of ethnic minority groups in the UK: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Growing ethnic diversity in the UK has made it increasingly important to determine the presence of ethnic health inequalities. There has been no systematic review that has drawn together research on ethnic differences in mortality in the UK. METHODS: All types of observational studies that compare all-cause mortality between major ethnic groups and the white majority population in the UK will be included. We will search Medline (OvidSP), Embase (OvidSP), Scopus and Web of Science and search the grey literature through conference proceedings and online thesis registries. Searches will be carried out from inception to 2 August 2019 with no language or other restrictions. Database searches will be repeated prior to publication to identify new articles published since the initial search. We will conduct forward and backward citation tracking of identified references and consult with experts in the field to identify further publications and ongoing or unpublished studies. Two reviewers will independently screen studies and extract data. Two reviewers will independently assess the quality of included studies using the Newcastle-Ottawa Scale. If at least two studies are located for each ethnic group and studies are sufficiently homogeneous, we will conduct a meta-analysis. If insufficient studies are located or if there is high heterogeneity we will produce a narrative summary of results. ETHICS AND DISSEMINATION: As no primary data will be collected, formal ethical approval is not required. The findings of this review will be disseminated through publication in peer reviewed journals and conference presentations. PROSPERO REGISTRATION NUMBER: CRD42019146143.
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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 | 0.102 | 0.093 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.019 | 0.014 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.075 | 0.014 |
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".