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Record W4366742601 · doi:10.1136/bmjopen-2023-071879

Country learning on maintaining quality essential health services during COVID-19 in Timor-Leste: a qualitative analysis

2023· article· en· W4366742601 on OpenAlexaff
Melissa Kleine-Bingham, Gregorio Rangel, Diana Sarakbi, Treasa Kelleher, Nana Afriyie Mensah Abrampah, Matthew Neilson, Oriane Bodson, Philippa White, Vinay Bothra, Helder M. de Carvalho, Feliciano da C.A. Pinto, Shamsuzzoha B Syed

Bibliographic record

VenueBMJ Open · 2023
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsQueen's University
FundersWorld Health OrganizationRockefeller Foundation
KeywordsMedicineTimor lesteCoronavirus disease 2019 (COVID-19)Qualitative researchPandemic2019-20 coronavirus outbreakQuality (philosophy)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Public healthCoronavirus InfectionsVirologyNursingEconomic growthInfectious disease (medical specialty)PathologyDiseaseOutbreak

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.007
Scholarly communication0.0030.003
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.097
GPT teacher head0.536
Teacher spread0.439 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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