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Record W3162355189

Association between maternal health status and birth outcomes in the Nelson Mandela Bay Health District

2018· article· en· W3162355189 on OpenAlexaboutno aff
Althea Anita Hawkins

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsnot available
Fundersnot available
KeywordsLow birth weightMedicineDemographyQuarter (Canadian coin)Birth orderBirth weightEnvironmental healthPregnancyPopulationGeography
DOInot available

Abstract

fetched live from OpenAlex

In 2011, the South African low birth weight rates varied between 9% and 15.5%, according to different sources. This means that about one out of every ten babies born alive weighed less than 2500g. Furthermore, six of South Africa’s nine provinces, including the Eastern Cape, reported low birth weight rates equal or higher than the national average. These figures raise serious concerns about the health status of infants, their chances of survival and their quality of life, particularly in provinces with a high incidence of low birth weight. Literature has linked the maternal health status to adverse birth outcomes. Statistics from the district office of the Nelson Mandela Bay Health District (NMBHD) indicates that for the fourth quarter of 2015, between 16.65 and 20.9% low birth weight infants were born. However, limited information is available regarding the causes and maternal health status of the mothers of the infants born with adverse birth outcomes in the Nelson Mandela Bay Health District (NMBHD). The objective of the research study is to investigate the associations between maternal health status and birth outcomes in order to identify the major drivers of adverse birth outcomes in NMBHD. The study used a quantitative research approach. In order to enhance the design, the researcher used an explorative, descriptive, cross-sectional, contextual and survey research design. The study was conducted at the regional hospital in Nelson Mandela Bay Health District (NMBHD) and Midwifery Obstetric Units (MOU). The participants were selected using a convenient and purposive sampling technique. A structured, self-administered questionnaire was used as the data collection tool. A statistician assisted with the data analysis. Descriptive and inferential statistics were used. The researcher ensured that ethical considerations were maintained throughout the study to protect the participants. Reliability and validity were also ensured throughout the study. The total sample of the study was 207 and the mean age of the participants was 26,9 years. Hypertension and HIV were the conditions most diagnosed prior to, and during, pregnancy. Most of the delivered infants were females. The findings of the study revealed a significant association between maternal diabetes, maternal hypertension and the infants’ birth weight. Additional findings iv revealed that independent of gestational age, mothers with hypertension are likely to deliver low birth weight (LBW) infants. Antenatal care is of the utmost importance during pregnancy and special attention should be given to the management of hypertension. The researcher developed recommendations for primary health care (PHC) nurses in antenatal clinics (ANC) to address the management of the major maternal drivers of LBW infants in order to decrease and prevent adverse birth 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 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 score0.378
Threshold uncertainty score0.751

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 studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.351
Teacher spread0.323 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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