Exploring Factors Associated with Women’s Willingness to Provide Digital Fingerprints in Accessing Healthcare Services: A Cross-Sectional Study in Urban Slums of Bangladesh
Bibliographic record
Abstract
Digital fingerprints are increasingly used for patient care and treatment delivery, health system monitoring and evaluation, and maintaining data integrity during health research. Yet, no evidence exists about the use of fingerprinting technologies in maternal healthcare services in urban slum contexts, globally. The present study aimed to explore the recently delivered women's willingness to give digital fingerprints to community health workers to access healthcare services in the urban slums of Bangladesh and identify the associated factors. Employing a two-stage cluster random sampling procedure, we chose 458 recently delivered women from eight randomly selected urban slums of Dhaka city, Bangladesh. Chi-square tests were performed for descriptive analyses, and binary logistic regression analyses were performed to explore the factors associated with willingness to provide fingerprints. Overall, 78% of the participants reported that they were willing to provide digital fingerprints if that eased access to healthcare services. After adjusting for potential confounders, the sex of the household head, family type, and household wealth status were significantly associated with the willingness to provide fingerprints to access healthcare services. The study highlighted the potentials of using fingerprints for making healthcare services accessible. Focus is needed for female-headed households, women from poor families, and engaging husbands and in-laws in mobile health programs.
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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 | 0.003 |
| 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 | 0.001 | 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".