Resilience of primary healthcare system across low- and middle-income countries during COVID-19 pandemic: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Globally, the coronavirus disease 2019 (COVID-19) pandemic tested the resilience of the health system and its shock-absorbing capacity to continue offering healthcare services. The available evidences does not provide comprehensive insight into primary health care (PHC) system functioning across low- and middle- income countries (LMICs) during the pandemic. Therefore, the objective of this scoping review was to generate evidence on the resilience of PHC systems in LMICs during the COVID-19 pandemic. METHODS: A scoping review was carried out utilizing an iterative search strategy using the National Library of Medicine (NLM) and the WHO COVID-19 electronic databases. Data from the identified studies in LMICs were charted in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist in the first step. The analysis framework was adapted and modified using COVID-19 and health systems resilience framework developed by Sagan et al., Blanchet et al., and the WHO position paper on 'Building health systems resilience for universal health coverage and health security during the COVID-19 pandemic and beyond'. A total of 26 documents were included on the basis of predefined eligibility criteria for our analysis. RESULTS: Our review explored data from 44 LMICs that implemented strategies at the PHC level during the COVID-19 pandemic. Most of the LMICs developed national guidelines on sexual, reproductive, maternal, newborn, child, and adolescent health (SRMNCAH). Most of the countries also transformed and reoriented PHC service delivery by introducing digital healthcare services to continue essential services. Task shifting, task sharing, and redeployment of retired staff were some frequently adopted health workforce strategies adopted by most of the countries. Only a few of the countries demonstrated the availability of necessary monetary resources to respond to the pandemic. CONCLUSIONS: The functionality of the PHC system during the COVID-19 pandemic was demonstrated by a variety of resilience strategies across the six building blocks of the health system. To strengthen PHC resilience, we recommend strengthening community-based PHC, cross-sectoral collaboration, establishing surveillance systems, capacity building in financial risk planning, and investing in strengthening the digital healthcare system.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.017 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".