Disruption of diabetes and hypertension care during the COVID-19 pandemic and recovery approaches in the Latin America and Caribbean region: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic significantly disrupted primary healthcare globally, with particular impacts on diabetes and hypertension care. This review will examine the impact of pandemic disruptions of diabetes and hypertension care services and the evidence for interventions to mitigate or reverse pandemic disruptions in the Latin America and Caribbean (LAC) region. METHODS AND ANALYSES: This scoping review will examine care delivery disruption and approaches for recovery of primary healthcare in the LAC region during the COVID-19 pandemic, focusing on diabetes and hypertension awareness, detection, treatment and control. Guided by Arksey and O'Malley's scoping review methodology framework, this protocol adheres to the Joanna Briggs Institute guidelines for scoping review protocols and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidance for protocol development and scoping reviews. We searched MEDLINE, CINAHL, Global Health, Embase, Cochrane, Scopus, Web of Science and LILACS for peer-reviewed literature published from 2020 to 12 December 2022 in English, Spanish or Portuguese. Studies will be considered eligible if reporting data on pandemic disruptions to primary care services within LAC, or interventions implemented to mitigate or reverse pandemic disruptions globally. Studies on COVID-19 or acute care will be excluded. Two reviewers will independently screen each title/abstract for eligibility, screen full texts of titles/abstracts deemed relevant and extract data from eligible full-text publications. Conflicts will be resolved through discussion and with the help of a third reviewer. Appropriate analytical techniques will be employed to synthesise the data, for example, frequency counts and descriptive statistics. Quality will be assessed using the Newcastle Ottawa Quality Assessment Scale. ETHICS AND DISSEMINATION: No ethics approval was needed as this is a scoping review of published literature. Results will be disseminated in a report to the World Bank and the Pan American Health Organization, in peer-reviewed scientific journals, and at national and international conferences.
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 | 0.123 | 0.135 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.089 | 0.022 |
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".