Country Learning on Maintaining Quality Essential Health Services (EHS) during COVID-19 in Timor-Leste: A mixed methods qualitative analysis
Bibliographic record
Abstract
ABSTRACT Objective This research study examines the enabling factors, strengths, and challenges experienced by the Timor-Leste health system as it sought to maintain quality essential health services (EHS) during the COVID-19 pandemic. Design A mixed methods qualitative analysis Setting National, municipal, facility levels in Baucau, Dili and Ermera Municipalities in TLS Participants Key informant interviews (n=40) and focus group discussions (n=6) working to maintain quality EHS in TLS. Results A reduction in people accessing general health services was observed in 2020, reportedly due to fears of contracting COVID-19 in healthcare settings, limited resources (eg. human resources, personal protective equipment, clinical facilities, etc) and closure of health services. However, improvements in maternal child health services simultaneously improved in the areas of skilled birth attendants, prenatal coverage, and vitamin A distribution, for example. Five themes emerged as enabling factors for maintaining quality EHS including 1) high level strategy for maintaining quality EHS, 2) implementation of quality activities across the three levels of the health system, 3) measurement for quality and factors affecting service utilization 4) the positive impact of quality improvement leadership in health facilities during COVID-19, and 5) learning from each other for maintaining quality EHS now and for the future. Other countries may benefit from the challenges, strengths and enablers found on planning for quality. Conclusion The maintenance of quality essential health services (EHS) is critical to mitigate adverse health effects from the COVID-19 pandemic. When quality health services are delivered prior to and maintained during public health emergencies, they build trust within the health system and promote healthcare seeking behavior. Planning for quality as part of emergency preparedness can facilitate a high standard of care by ensuring health services continue to provide a safe environment, reduce harm, improve clinical care, and engage patients, facilities, and communities. DATA SHARING All data is kept with MBK and GR and is available upon request. The dataset analysis is available from the corresponding author upon reasonable request. QUALITATIVE CHECKLIST The Standards for Reporting Qualitative Research (SRQR) checklist was used for this original research. STRENGTHS AND LIMITATIONS OF THIS STUDY The qualitative data gave detailed insights to the operationalization of key strategic COVID-19 emergency documents and the national quality implementation strategy. Data collection was performed in three out of thirteen municipalities, including the largest metropolitan city of Dili. The qualitative research was conducted in the participants native language (Tetum). Not all pre-identified national level KII participants were available to provide feedback.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".