Factors associated with mobile phone usage to access maternal and child healthcare among women of urban slums in Dhaka, Bangladesh: a cross-sectional study
Bibliographic record
Abstract
INTRODUCTION: With the acute shortage of human resources and infrastructure, mobile phones can be a critical tool for accessing health services and strengthening health systems in Bangladesh. Yet, there is a scarcity of evidence on the use of mobile phones in this context for accessing health services. In this study, we sought to explore the current use of mobile phones for accessing maternal and child healthcare and its determinants among recently delivered women in urban slums of Bangladesh. METHODS: The data were collected through interviewing 800 recently delivered women from eight slums of Dhaka city of Bangladesh during May and June 2018. The study followed a cross-sectional design and a two-stage cluster random sampling procedure was followed. A pretested structured questionnaire was employed to collect information. Chi square tests were performed for descriptive analyses and a multilevel binary logistic regression model was executed to explore the determinants of mobile phone usage for accessing maternal and childcare among the participants. RESULTS: Overall, 73.8% of study participants used mobile phones for accessing maternal and child healthcare. After adjusting for potential confounders, participants' age, husband's occupation, sex of household head, women's ownership of mobile phones and household wealth status were found to be significantly associated with higher odds of using mobile phones to access maternal and child healthcare. CONCLUSION: The study highlighted the possibility of implementing large-scale mobile health (mHealth) interventions in slum settlements for accessing maternal and child healthcare and is a sustainable mitigation strategy for the acute health worker crisis in Bangladesh. The findings of this study are particularly crucial for policymakers and practitioners while they revise the health policy to incorporate mHealth interventions as highlighted in the recently initiated Digital Health Strategy of Bangladesh.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".