Learning From Countries on Measuring and Defining Community-Based Resilience in Health Systems: Voices From Nepal, Sierra Leone, Liberia, and Ethiopia
Bibliographic record
Abstract
BACKGROUND: The best approach for defining and measuring community healthcare (CHC) resilience in times of crisis remains elusive. We aimed to synthesise definitions and indicators of resilience from countries who had recently undergone shocks (ie, outbreaks and natural disasters). METHODS: We purposively selected four countries that had recently or were currently experiencing a shock: Nepal, Ethiopia, Sierra Leone, and Liberia. Focus group discussions (FGDs) and key informant interviews (KIIs) were conducted with participants at the community, facility, district, sub-national, national, and international levels. Interviews and discussions were translated and transcribed verbatim. Data were open coded in ATLAS.ti using a grounded theory approach and were thematically collated to a pre-specified framework. RESULTS: A total of 486 people participated in the study (n=378 community members, n=108 non-community members). Emergent themes defining CHC resilience included: the importance of communities, health system characteristics, learning from shocks, preventing and preparing for shocks, and considerations for sustainability and intersectoral engagement. Participants identified 193 potential indicators for measuring resilience, which fell into the domains of: (1) preparedness, (2) response and recovery, (3) communities, (4) health systems, and (5) intersectoral engagement. CONCLUSION: Despite varying definitions and understanding of the concept of resilience, community-centred responses to shocks were key in building resilience. Further insight is needed into how the definitions and indicators identified in this study compare to other shocks and contexts and can be used to further our understanding of health system resilience. Metrics and definitions could assist policy-makers, researchers, and practitioners in evaluating the readiness of systems to respond to shocks and to allow comparability across health systems. We must build health systems that can continue to function and ensure quality, equity, community-focused care, and engagement, regardless of the pressures put upon them and ensure they are linked to strong primary healthcare.
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 | 0.023 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".