Evidence of integrated health service delivery during COVID-19 in low and lower-middle-income countries: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: The importance of integrated, people-centred health systems has been recognised as a central component of Universal Health Coverage. Integration has also been highlighted as a critical element for building resilient health systems that can withstand the shock of health emergencies. However, there is a dearth of research and systematic synthesis of evidence on the synergistic relationship between integrated health services and pandemic preparedness, response, and recovery in low-income and lower-middle-income countries (LMICs). Thus, the authors are organising a scoping review aiming to explore the application of integrated health service delivery approaches during the emerging COVID-19 pandemic in LMICs. METHODS AND ANALYSIS: This scoping review adheres to the six steps for scoping reviews from Arksey and O'Malley. Peer-reviewed scientific literature will be systematically assembled using a standardised and replicable search strategy from seven electronic databases, including PubMed, Embase, Scopus, Web of Science, CINAHL Plus, the WHO's Global Research Database on COVID-19 and LitCovid. Initially, the title and abstract of the collected literature, published in English from December 2019 to June 2020, will be screened for inclusion which will be followed by a full-text review by two independent reviewers. Data will be charted using a data extraction form and reported in narrative format with accompanying data matrix. ETHICS AND DISSEMINATION: No ethical approval is required for the review. The study will be conducted from June 2020 to May 2021. Results from this scoping review will provide a snapshot of the evidence currently being generated related to integrated health service delivery in response to the COVID-19 pandemic in LMICs. The findings will be developed into reports and a peer-reviewed article and will assist policy-makers in making pragmatic and evidence-based decisions for current and future pandemic responses.
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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 | 0.150 | 0.135 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.016 | 0.022 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.074 | 0.017 |
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".