Mobile Life Skills Education Adoption Among Internally Displaced Persons in Northern Nigeria and Health Systems Implications for Equitable Mental Health Support: A cross-sectional study
Bibliographic record
Abstract
ABSTRACT Introduction Internally displaced persons (IDPs) face significant mental health challenges amidst severely disrupted health systems. Digital interventions offer promising pathways to deliver psychosocial support, yet critical gaps remain in understanding what determines their acceptability and adoption among vulnerable populations in conflict settings. Objective This study investigates the sociodemographic, psychosocial, technological, and cultural determinants of interest in mobile-based life skills education (mLSE) among IDPs in Nigeria, integrating four theoretical frameworks to generate actionable insights for equitable digital mental health service delivery in fragile settings. Methods We analyzed cross-sectional data from 220 IDPs in the Durumi and Wassa camps of Abuja, Nigeria. Variable selection employed elastic net regression, identifying 22 key predictors. Modified robust Poisson regression estimated prevalence ratios for mLSE interest, with interaction effects modeled to capture demographic intersectionalities. Results Among participants, 48.6% expressed interest in mLSE, with significant disparities across age, education, and camp location. Young adults aged 20-24 with prior counseling experience showed substantially higher interest (APR=3.49, 95% CI 1.72-7.10), while males with counseling history demonstrated markedly lower engagement (APR=0.33, 95% CI 0.19-0.57). Secondary education strongly predicted interest (APR=2.27, 95% CI 1.59-3.26), as did residence in the Wassa camp (APR=1.64, 95% CI 1.21-2.23). Notably, males aged 30-34 exhibited minimal interest (APR=0.09, 95% CI 0.01-0.75), revealing critical gender-age intersections. Conclusions These findings reveal actionable patterns for strengthening digital mental health service delivery in displacement settings. Health systems in fragile contexts must develop digitally delivered interventions that are culturally responsive, gender-sensitive, and age-appropriate, while addressing educational and technological barriers. Leveraging prior service engagement appears critical for sustainable implementation. This study provides a roadmap for policymakers and implementers to design equitable digital mental health interventions that address the disparate needs of displaced populations.
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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 | 0.002 |
| 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 | 0.001 |
| 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".