Social Acceptance of Mobile Health Technologies Among the Young Population in Nigeria
Bibliographic record
Abstract
Mobile devices are widely used in modernizing healthcare delivery because of their unique features related to accessibility, virtual interaction, and connectivity. While developing countries, with limited resources, strive to achieve high healthcare standards, and mobile health (mHealth) solutions could transform healthcare delivery systems in these countries, their functionality is currently limited. This study investigates the potential systematic introduction of mHealth services and their social acceptability in developing countries, with a particular focus on Nigeria. This cross-sectional study was conducted with a sample of university students. Structural equation modeling was used to test the study hypotheses, and descriptive statistics were used to analyze the sociodemographic characteristics of the participants. Psychological and personal characteristics, environmental characteristics, and conditions of use associated with mHealth technology adoption were examined based on eight constructs (health consciousness, trust, social influence, perceived risk, performance expectancy, facilitating conditions, effort expectancy, and behavioral intention). The results indicate that trust and performance expectancy are significant predictors of mHealth acceptance in the surveyed population. The future acceptance of mHealth among young people in developing countries holds great significance for improving healthcare delivery, addressing the unique challenges faced by developing countries, and leveraging the preferences of young individuals, which could contribute to the advancement of mHealth solutions and enhance healthcare accessibility. The findings shed light on the acceptance of mHealth technologies among the young populations of developing countries, with implications for future efforts to improve healthcare delivery and address the healthcare challenges of these countries.
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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 | 0.000 |
| 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".