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Record W4388410725 · doi:10.61386/imj.v12i2.366

Self-care practices of pregnant women: A qualitative study in a Nigerian rural community

2023· article· en· W4388410725 on OpenAlexfundno aff
Etokidem AJ, Benson Obu

Bibliographic record

VenueIbom Medical Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersGrand Challenges CanadaGovernment of Canada
KeywordsPregnancyContext (archaeology)MedicineFocus groupQualitative researchHealth careFamily medicineReproductive healthNursingRural areaAbandonment (legal)PsychologyEnvironmental healthPopulationGeographyPolitical science

Abstract

fetched live from OpenAlex

Context: Inadequate self-care during pregnancy is a contributor to the poor maternal health indices in Cross River State; including the high maternal mortality ratio of 2,000/100,000 live births.
 Objectives: The objectives of the study were to identify self-care practices adopted by women during pregnancy and delivery and to identify barriers to quality self care.
 Methods: Focus Group Discussions, Key informant interviews and in-depth interviews were conducted among pregnant women, women of reproductive age and other stakeholders in Biase Local Government Area of Cross River State.
 Results: The study revealed inadequate knowledge and practice of self-care during pregnancy. Some pregnant women were unable to recognize early signs of pregnancy while others, especially young unmarried girls, tried to hide the pregnancy. Barriers to effective self-care identified included myths and misconceptions, especially the belief that health-related events during pregnancy are caused by witches and wizards, lack of preparation for pregnancy and abandonment of pregnant women by partners, usually due to unwillingness and/or inability to father the child.
 Conclusion: There is need for pre-marital and pre-natal counselling and health education so as to address identified gaps in knowledge and practice and lack of male involvement in maternal healthcare.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 integrity0.0000.001
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 head0.045
GPT teacher head0.432
Teacher spread0.388 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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