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Record W2766530102 · doi:10.12968/ajmw.2017.11.4.176

Narratives of labour and childbirth in women with obstetric fistula in Kenya

2017· article· en· W2766530102 on OpenAlexafffund
Anne M. Khisa, Grace Omoni, Rachel F. Spitzer, Isaac K. Nyamongo

Bibliographic record

VenueAfrican Journal of Midwifery and Women s Health · 2017
Typearticle
Languageen
FieldMedicine
TopicUreteral procedures and complications
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersInternational Development Research CentreStyrelsen för Internationellt Utvecklingssamarbete
KeywordsChildbirthFistulaReferralNarrativeMedicineObstetricsObstructed labourGrounded theoryNursingPregnancyQualitative researchSociologySurgeryCaesarean section

Abstract

fetched live from OpenAlex

Background: Obstetric fistula is caused by prolonged obstructed labour, and affects about 1% of women in Kenya. Although early treatment is recommended, women live with the condition for many years and endure physical, psychological and social suffering. Aims: To document the experiences of women with fistula illness. Methods: Using grounded theory methodology, narratives focusing on labour and childbirth were obtained from a purposive sample of 121 patients with obstetric fistula in 3 hospitals in Kenya. Narratives were analysed using a three-dimensional framework: Interaction, Continuity and Place. Findings: Six emergent themes predicting occurrence of fistula were identified. These were: people who assisted the labour process (interaction); time spent in labour; birth outcomes for baby (continuity); mode of delivery; referral of women in obstructed labour; and place of delivery. Conclusions: These findings have potential to inform the development of a midwifery early screening intervention for fistula care in Kenya.

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)0.000
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 score0.087
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.277
Teacher spread0.263 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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