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Record W4385563845 · doi:10.1016/j.jss.2023.07.014

Determinants of Access to Essential Surgery in the Democratic Republic of Congo

2023· article· en· W4385563845 on OpenAlexafffundabout
Luc Malemo Kalisya, Ava Yap, Boniface Mitume, Christian Salmon, Kambale Karafuli, Dan Poenaru, Rosebella Onyango

Bibliographic record

VenueJournal of Surgical Research · 2023
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsMcGill University
FundersInternational Development Research Centre
KeywordsMedicineDistrustPopulationDemocracyHealth careQuarter (Canadian coin)Family medicineNursingEnvironmental healthEconomic growthPolitical scienceGeography

Abstract

fetched live from OpenAlex

INTRODUCTION: In the Democratic Republic of Congo (DRC), the determinants and barriers of essential surgical care are not well described, hindering efforts to improve national surgical programs and access. METHODS: A cross-sectional study evaluated access to essential surgery in the Butembo and Katwa health zones in the North Kivu province of DRC. A double-clustered random sample of community members was surveyed using questions derived from the Surgeons OverSeas Surgical Needs Assessment Survey, a validated tool to determine the reasons for not seeking, reaching, or receiving a Bellwether surgery (i.e., caesarean delivery, laparotomy, and external fixation of a fracture) when needed. RESULTS: Overall, 887 households comprising 5944 community members were surveyed from April to August 2022. Six percent (n = 363/5944) of the study population involving 35% (n = 309/887) households needed a Bellwether surgery in the previous year, 30% (n = 108/363) of whom died. Of those who needed surgery, 25% (n = 78) did not go to the hospital to seek care and were more likely to find transportation unaffordable (P = 0.042). The most common reasons for not seeking care were lack of funds for hospitalization, prior poor hospital experience, and fear of hospital care. CONCLUSIONS: Access and delivery of essential surgery are drastically limited in the North Kivu province of the DRC, such that a quarter of households needing surgery fails to seek surgical care. Poor access was predominantly driven by households' inability to pay for surgery and community distrust of the hospital system.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.237
GPT teacher head0.515
Teacher spread0.278 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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 routes3
Has abstractyes

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