Mapping health service coverage inequalities in Africa: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Addressing inequities in health service coverage is a global priority, especially with the resurgence of interest in universal health coverage. However, in Africa, which has the lowest health service coverage index, there is limited information on the progress of countries in addressing inequalities related to health services. Thus, we seek to map the evidence on inequalities in health service coverage in Africa. METHODS AND ANALYSIS: We will conduct a scoping review following the Joanna Briggs Institute Manual for Evidence Synthesis. We preregistered this protocol with the Open Science Framework on 26 July 2022 (https://osf.io/zd5bt). We will consider any empirical research that assesses inequalities in relation to services for reproductive, maternal, newborn and child health (eg, family planning), infectious diseases (eg, tuberculosis treatment) and non-communicable diseases (eg, cervical cancer screening) in Africa. We will search MEDLINE, Embase, Web of Science, CINAHL, PsycINFO and Cochrane Library from their inception onwards. We will also hand-search Google and Global Index Medicus, and screen reference lists of relevant studies. We will evaluate studies for eligibility and extract data from included studies using pre-piloted and standardised forms. We will further extract a core set of health service coverage indicators, which are disaggregated by place of residence, race/ethnicity/culture, occupation, gender, religion, education, socioeconomic status and social capital plus equity stratifiers. We will summarise data using a narrative approach involving thematic syntheses and descriptive statistics. We will report our findings according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. ETHICS AND DISSEMINATION: Ethical approval is not required as primary data will not be collected. This work will contribute to identifying knowledge gaps in the evidence of inequalities in health service coverage in Africa, and propose strategies that could help overcome current challenges. We will disseminate our findings to knowledge users through a publication in a peer-reviewed journal and organisation of workshops.
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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 | 0.127 | 0.136 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.124 | 0.028 |
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".