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Record W2897938195 · doi:10.2196/pediatrics.9618

Mobile Phone Access and Willingness Among Mothers to Receive a Text-Based mHealth Intervention to Improve Prenatal Care in Northwest Ethiopia: Cross-Sectional Study

2018· article· en· W2897938195 on OpenAlexaffvenue
Berhanu Fikadie Endehabtu, Adane Nigusie Weldeab, Martin C. Were, Richard Lester, Binyam Tilahun

Bibliographic record

VenueJMIR Pediatrics and Parenting · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of British Columbia
FundersUniversity of Gondar
KeywordsmHealthPsychological interventionMedicineDescriptive statisticsContext (archaeology)Prenatal careShort Message ServiceFamily medicineMobile phoneReceiptHealth careIntervention (counseling)Cross-sectional studyNursingPopulationEnvironmental healthGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Maternal mortality remains high in many low- and middle-income countries where limited access to health services is linked to low antenatal care utilization. Effective communication and engagement with care providers are vital for the delivery and receipt of sufficient health care services. There is strong evidence that simple text-based interventions can improve the prenatal care utilization, but most mobile health (mHealth) interventions are not implemented on a larger scale owing to the lack of context and preliminary evidence on how to make the transition. OBJECTIVE: The objective of this study was to determine access to mobile phones by pregnant women attending antenatal care as well as willingness to receive a text message (short message service, SMS)-based mHealth intervention for antenatal care services and identify its associated factors among pregnant women attending an antenatal care clinic in Gondar Town Administration, Northwest Ethiopia, Africa. METHODS: A cross-sectional quantitative study was conducted among 422 pregnant women attending antenatal care from March 27 to April 28, 2017. Data were collected using structured questionnaires. Data entry and analysis were performed using Epi-Info version 7 and SPSS version 20, respectively. In addition, descriptive statistics and bivariable and multivariable logistic regression analyses were performed. Furthermore, odds ratio with 95% CI was used to identify factors associated with the willingness to receive a text message-based mHealth intervention. RESULTS: A total of 416 respondents (response rate 98.6%, 416/422) were included in the analysis. About 76.7% (319/416) of respondents owned a mobile phone and 71.2% (296/416) were willing to receive an SMS text message. Among the mobile phone owners, only 37.6% (120/319) were having smartphones. Of all women with mobile phones, 89.7% (286/319) described that they are the primary holders of these phones and among them, 85.0% (271/319) reported having had the same phone number for more than a year. Among the phone owners, 90.0% (287/319) described that they could read and 86.8% (277/416) could send SMS text messages using their mobile phones in their day-to-day activities. Among pregnant women who were willing to receive SMS text messages, about 96.3% (285/296) were willing to receive information regarding activities or things to avoid during pregnancy. Factors associated with willingness were youth age group (adjusted odds ratio [AOR] 2.869, 95% CI 1.451-5.651), having attained secondary and higher educational level (AOR 4.995, 95% CI 1.489-14.773), and the frequency of mobile phone use (AOR 0.319, 95% CI 0.141-0.718). CONCLUSIONS: A high proportion of pregnant women in an antenatal care clinic in this remote setting have a mobile phone and are willing to receive an SMS text message-based mHealth intervention. Age, educational status, and the frequency of mobile phone use are significantly associated with the willingness to receive SMS text message-based mHealth interventions.

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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0010.000

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 head0.034
GPT teacher head0.435
Teacher spread0.400 · 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

Citations33
Published2018
Admission routes2
Has abstractyes

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