Country learning on maintaining quality essential health services during COVID-19 in Timor-Leste: a qualitative analysis
Bibliographic record
Abstract
OBJECTIVE: This case study examines the enabling factors, strengths, challenges and lessons learnt from Timor-Leste (TLS) as it sought to maintain quality essential health services (EHS) during the COVID-19 pandemic. DESIGN: A qualitative case study triangulated information from 22 documents, 44 key informant interviews and 6 focus group discussions. The framework method was used to thematically examine the factors impacting quality EHS in TLS. SETTING: National, municipal, facility levels in Baucau, Dili and Ermera municipalities in TLS. RESULTS: Based on the TLS National Health Statistics Reports, a reduction in outpatient, emergency department and primary care service delivery visits was observed in 2020 when compared with 2019. However, in contrast, maternal child health services simultaneously improved in the areas of skilled birth attendants, prenatal coverage and vitamin A distribution, for example. From the thematic analysis, five themes emerged as contributing to or impeding the maintenance of quality EHS including (1) high-level strategy for maintaining quality EHS, (2) measurement for quality and factors affecting service utilisation, (3) challenges in implementation of quality activities across the three levels of the health system, (4) the impact of quality improvement leadership in health facilities during COVID-19 and (5) learning systems for maintaining quality EHS now and for the future. CONCLUSION: The maintenance of quality 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 behaviour. 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.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".