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Record W2739407948 · doi:10.1177/1049732317717695

The Challenges of Providing Postpartum Education in Dar es Salaam, Tanzania: Narratives of Nurse-Midwives and Obstetricians

2017· article· en· W2739407948 on OpenAlexaff
Lilian Teddy Mselle, Megan Aston, Thecla W. Kohi, Columba Mbekenga, Danielle Macdonald, Maureen White, Sheri Price, Gail Tomblin Murphy, Shawna O’Hearn, Keisha Jefferies

Bibliographic record

VenueQualitative Health Research · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTanzaniaNursingMedicineQualitative researchDar es salaamMaternity carePostpartum periodNarrativeHealth careFamily medicineObstetricsPregnancy

Abstract

fetched live from OpenAlex

Postpartum education can save lives of mothers and babies in developing countries, and the World Health Organization recommends all mothers receive three postpartum consultations. More information is needed to better understand how postpartum education is delivered and ultimately improves postpartum health outcomes. The purpose of this qualitative study was to examine how postpartum care was delivered in three postnatal hospital clinics in Dar es Salaam, Tanzania. Semistructured interviews with 10 nurse-midwives and three obstetricians were conducted. Feminist poststructuralism guided the research process. Postpartum education was seen to be an urgent matter; there was a lack of supportive resources and infrastructure in the hospital clinics, and nurse-midwives and obstetricians had to negotiate conflicting health and traditional discourses using various strategies. Nurse-midwives and obstetricians are well positioned to deliver life-saving postpartum education; however, improvements are required including increased number of nurse-midwives and obstetricians.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.224
GPT teacher head0.556
Teacher spread0.331 · 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 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

Citations20
Published2017
Admission routes1
Has abstractyes

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