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Record W2025930434 · doi:10.12927/whp.2009.21039

Deliveries Among Adolescent Mothers in Rural Bangladesh: Who Provides Assistance?

2009· article· en· W2025930434 on OpenAlexvenueno aff
Md. Saifur Rahman

Bibliographic record

VenueWorld health & population · 2009
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
Fundersnot available
KeywordsUnit (ring theory)NursingPolitical scienceNursing researchAdministration (probate law)Developing countryHealth carePeer reviewMedicineEconomic growthPsychology

Abstract

fetched live from OpenAlex

OBJECTIVES: This paper sought to identify factors associated with modes of delivery assistance among adolescent mothers in rural Bangladesh. METHODOLOGY: Bangladesh Demographic and Health Survey of 2004 data for the last 5 years (N = 867) were used. Univariate statistical analysis and multivariate logistic regression methods were employed in analyzing the data. RESULTS: We observed that almost all adolescent deliveries (93.6 %) took place at home, and most (80.1%) were assisted by untrained traditional birth attendants, relatives or neighbours. Only 8.8% were attended by medically trained persons. Main factors affecting delivery practices among adolescents were mass media exposure, parents' education, antenatal care received, type of toilet facilities and visits by family planning workers (FPW), wanted last child and told about pregnancy complications. CONCLUSIONS: RESULTS indicate several policy options to improve outcomes for adolescent mothers: (a) create awareness of appropriate behaviours during pregnancy, delivery and post-partum period, (b) ensure maternal healthcare centres are available, especially rurally, for antenatal care, expand and improve the quality of home births by trained providers and introduce post-partum visits, (c) increase the number of visits by family welfare visitors/family welfare assistants (FWV/FWA), and (d) emphasize adolescent education to make a lasting impact on the overall health of adolescent mothers.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.732

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 head0.014
GPT teacher head0.296
Teacher spread0.282 · 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 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

Citations23
Published2009
Admission routes1
Has abstractyes

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