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Record W4390646845 · doi:10.5463/thesis.556

Poverty and high parity in rural settings

2024· dissertation· en· W4390646845 on OpenAlexaff
Manuela Straneo

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsAthena Sustainable Materials Institute
FundersEuropean Commission
KeywordsTanzaniaChildbirthPovertyParity (physics)MedicineHealth facilityHealth careSocioeconomicsEnvironmental healthDemographyPregnancyPopulationEconomic growthHealth servicesSociologyEconomics

Abstract

fetched live from OpenAlex

In this thesis I have studied where women give birth within the health system in sub-Saharan Africa, firstly in Tanzania and then in other countries, focusing on the use of hospitals in rural settings. Though childbirth care is available at different levels of the health system, hospitals are generally where advanced management of childbirth complications is available. This is particularly important for women with risk factors, at greater risk of complications and death. The research has examined the interaction of biological risk (high parity) with socio-economic vulnerability (poverty), identifying a subgroup of women who are poor, rural and of high parity who, despite greater risk of childbirth complications, have lower use of hospitals. In Iringa rural district in Tanzania, with nearly universal facility births coverage, my co-researchers and I found poor women were underrepresented in the only hospital in the district. This finding led to hypothesizing that women from poorer households were more likely to use primary care facilities for childbirth. Use of hospitals in all Tanzania was studied using nationally representative Demographic and Health Survey data. In rural Tanzania, we found that the effect of poverty on use of hospitals depended on the level of parity. Women who were both poor and at high parity used hospitals least: only around one in ten women who were poor and at high parity had given birth in a hospital in the period studied. The complex interacting and interdependant factors related to high parity, poverty and rurality are likely to be responsible. High parity, poor, rural women have remained marginalized in use of hospitals over the most recent 25 years for which data were available (1991-2016). This finding is particularly relevant as the country has been at the forefront in rolling out primary health care since independence. High population growth brought an increase of 35,000 births/year over this period. Health system expansion during this time to achieve universal coverage of essential services to a growing, mostly rural population prioritized primary care facilities compared to hospitals. Across a range of sub-Saharan African countries, we found the same pattern of low use of hospitals by high parity, poor, rural women. Using a simple, reproducible tool, some countries had comparatively higher socio-economic equality and use of hospitals by women at high parity. Malawi and Liberia, closely followed by Zimbabwe, the Gambia and Rwanda, ranked higher in a composite use and equity index. Further studies on policy in these countries are needed to identify policies and guidelines that facilitate equitable use of hospitals by women, particularly at high parity. Low use of hospitals by high parity women impacts perinatal mortality in SSA. In a study on births in 16 hospitals across four SSA countries (Benin, Malawi, Tanzania and Uganda), women of high parity had a high risk of intrapartum (fresh stillbirths and very early neonatal deaths) mortality when they reached hospitals following intrapartum referral. The risk of death of a baby born to a woman of high parity referred intrapartum was 2.5 times greater than that of high parity women who reached hospitals without referral. To reduce the risk associated with intrapartum referral among these women, measures are urgently needed to ensure all high parity women use hospitals for childbirth. Policy makers’ recognition of this marginalized group of women is urgently needed to put in place measures to mitigate their disadvantage and higher risk of adverse outcomes, including improved access to hospitals.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.005
GPT teacher head0.278
Teacher spread0.273 · 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 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
Published2024
Admission routes1
Has abstractyes

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