MétaCan
Menu
Back to cohort
Record W2898012895 · doi:10.2196/10645

Women Using Mobile Phones for Health Communication Are More Likely to Use Prenatal and Postnatal Services in Bangladesh: Cross-Sectional Study

2018· article· en· W2898012895 on OpenAlexaffvenue
Shangfeng Tang, Ghose Bishwajit, Md. Rakibul Hoque, Gang Hao, Sanni Yaya

Bibliographic record

VenueJMIR mhealth and uhealth · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCross-sectional studymHealthMobile phoneMedicineEnvironmental healthPsychologyInternet privacyNursingTelecommunicationsPsychological interventionComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: The public health system in Bangladesh has been struggling to provide coverage and utilization of basic maternal health care services in pursuit of achieving maternal and child mortality-related goals. Interestingly, the rapid popularization of mobile technology in the country is transforming the landscape of health care access and delivery. However, little is known regarding the use of mobile phones from the perspective of maternal health care service utilization. OBJECTIVE: In this study, we aimed to investigate the prevalence and sociodemographic pattern of mobile phone use for health services among women and relationship between the use of mobile phone use and the uptake of essential maternal health services (MHSs). METHODS: Cross-sectional data from the Bangladesh Demographic and Health Survey on 4494 mothers aged between 15 and 39 years were used in the analysis. Using mobile phones to get health services or advice was hypothesized to have a positive association with the uptake of basic MHSs (antenatal care, ANC, facility delivery services, postnatal care) and postnatal care for the newborn. Data were analyzed using bivariate and multivariable techniques. RESULTS: More than a quarter (1276/4494, 28.4%; 95% CI 26.8-30.3) of the women aged 15-39 years reported using mobile phones to get health services with significant sociodemographic variations in the use of mobile phones. Analysis of the specific purposes revealed that, in most cases, mobile phones were used to contact service providers and consult with the same about what to do, whereas a smaller proportion reported using mobile phone for the purposes of arranging money and transportation. Multivariable analysis showed that compared with respondents who reported not using mobile phones for health care services, those who used them had higher odds of making 3+ ANC visits and delivering at a health facility. The odds were slightly higher for rural residents than for those in the urban areas. CONCLUSIONS: The findings of this study conclude that women who use mobile phones are more likely to use ANC and professional delivery services than those who do not. More in-depth studies are necessary to understand the mechanism through which mobile phone-based services enhance the uptake of maternal health care.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.002
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 score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.108
GPT teacher head0.495
Teacher spread0.387 · 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

Labeled directly by 2 models reading the full record.

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

Citations28
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueJMIR mhealth and uhealthSame topicMobile Health and mHealth ApplicationsFrench-language works237,207