Mobile Technology Access and Use Among Adolescent Mothers in Lima, Peru: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Research shows promise for the use of mobile health interventions to improve access to care for mothers and infants. Although adolescent mothers in particular are comfortable with technology and often face barriers to accessing care, data on the use of digital interventions with young mothers are limited. OBJECTIVE: This study aims to examine technology access and use behavior among adolescent mothers in Lima, Peru, to inform the development of technology-mediated perinatal interventions for high-risk mothers and infants in low- and middle-income countries and other areas with limited access to care. METHODS: This mixed methods study consisted of a phone survey about technology access (N=29), focus group discussions with clinicians (N=25), and semistructured in-depth interviews with adolescent mothers (N=10) and their family members (N=8) in Lima. RESULTS: All adolescent mothers surveyed had access to a smartphone, and nearly half had access to a computer or tablet. However, participants reported a number of obstacles to consistent smartphone access related to the financial precarity of their situations. Examples of this included difficulty affording phone services, using shared plans, and losing smartphones because of theft. CONCLUSIONS: These findings indicate that adolescent mothers are connected to technology, highlighting the potential scalability of technology-based health interventions for adolescent mothers in low- and middle-income countries while identifying barriers that need to be addressed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".