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Record W2550886714 · doi:10.1016/j.ijgo.2016.08.019

Determinants of non‐medically indicated cesarean deliveries in Burkina Faso

2016· article· en· W2550886714 on OpenAlexafffund
Charles Kaboré, Valéry Ridde, Séni Kouanda, Isabelle Agier, Ludovic Queuille, Alexandre Dumont

Bibliographic record

VenueInternational Journal of Gynecology & Obstetrics · 2016
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health ResearchUNICEF
KeywordsMedicineSpousePsychological interventionObservational studyReferralSocioeconomic statusResidenceLogistic regressionAuditDemographyFamily medicineEnvironmental healthNursingPopulation

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify the factors associated with non-medically indicated cesarean deliveries (NMIC) in Burkina Faso in centers where user fees for cesarean delivery were partially removed. METHODS: We carried out a criteria-based audit in 22 referral hospitals, using data from a 6-month prospective observational study, to assess the proportion of NMIC. Multivariate logistic regression analyses were used to identify factors associated with NMIC. RESULTS: The decision of cesarean delivery was not medically indicated in 24% of cases. The factors independently associated with NMIC were urban residence (adjusted OR 1.55; 95% CI, 1.12-2.12; P=0.006), spouse's occupation other than breeder or farmer (aOR varying from 1.77 [95% CI, 1.19-2.62] to 2.15 [95% CI, 1.38-3.32] according to the profession), and cesarean decided by a general practitioner (aOR 1.61; 95% CI, 1.13-2.30; P=0.009). CONCLUSION: The high percentage of unnecessary cesarean deliveries is in contrast to the unmet needs of women who still deliver outside health facilities. NMIC is associated with both socioeconomic determinants and medical factors. Hence, interventions are needed to improve the skills of healthcare professionals and awareness of women concerning the risks associated with unnecessary cesarean delivery.

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)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.339
Teacher spread0.318 · 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.

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

Citations35
Published2016
Admission routes2
Has abstractyes

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