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Record W2793316391 · doi:10.1080/03630242.2018.1434591

Mothers’ perceptions and experiences of using maternal health-care services in Rwanda

2018· article· en· W2793316391 on OpenAlexafffund
Germaine Tuyisenge, Celestin Hategeka, Yvone Kasine, Isaac Luginaah, David F. Cechetto, Stephen Rulisa

Bibliographic record

VenueWomen & Health · 2018
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of British ColumbiaSimon Fraser UniversityCentre for Advancing Health OutcomesWestern University
FundersForeign Affairs and International Trade Canada
KeywordsFocus groupQualitative researchHealth carePerceptionMedicineMaternal healthDeveloping countryNursingMaternal morbidityEnvironmental healthHealth servicesPsychologyEconomic growthPregnancyPopulationBusinessSociology

Abstract

fetched live from OpenAlex

Reducing barriers to use maternal health care is one of the critical components to improving maternal health. Rwanda is among the countries that have made tremendous efforts to reduce maternal mortality. However, the current maternal mortality ratio is still high which calls for further efforts to be considered. This study used a qualitative approach to understand mothers' perceptions and experiences of using maternal health care in Rwanda. Using in-depth interviews and focus group discussions, data were collected in the Western and Eastern provinces of the country where forty-five women participated in the study from June to August 2014. This paper highlights perceptions of these participants regarding issues that contribute to suboptimal use of maternal health-care services. The geographical, financial, and social-cultural barriers that emerged in this study highlight the need to understand mothers' experiences and perceptions when using maternal health care as Rwanda and other countries strive to reduce negative maternal health outcomes.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score0.902

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 head0.015
GPT teacher head0.327
Teacher spread0.312 · 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

Citations32
Published2018
Admission routes2
Has abstractyes

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