eHealth literacy and digital health interventions: Key ingredients for supporting the mental health of displaced youth living in the urban slums of kampala, Uganda
Bibliographic record
Abstract
During and after displacement, many displaced youth face increased vulnerability to poor mental health and can encounter inaccurate or confusing health information. Digital tools create new opportunities to reach more of these youth with mental health interventions. Yet maximizing these tools' effectiveness among displaced youth requires understanding their eHealth literacy (eHEALS; i.e., the ability to find, understand, and appraise health information from electronic sources and apply this knowledge to a health problem). Thus, we conducted a community-based cross-sectional survey of 445 displaced youth (16–24 years) living in the slums of Kampala, Uganda to measure their eHEALS and its association with psychosocial wellbeing. Exploratory and confirmatory factor analysis identified a unidimensional measure of eHEALS. Structural equation modeling results indicated that eHEALS was not directly associated with depressive symptoms (β = .08, p = 0.15), but was significantly positively associated with resilience (β = .32, p < 0.001). Resilience was, in turn, significantly negatively associated with depressive symptoms (β = −.21, p < 0.001). The Sobel test for indirect effects confirmed that eHEALS indirectly negatively affected depressive symptoms through resilience (i.e., β indirect effect = −.07, p = 0.004). Our findings highlight the need for interventionists to develop contextualized eHealth interventions that facilitate displaced youth's ability to access, understand, and use health information to the best of their ability and optimally benefit from services. • Exploratory and confirmatory factor analysis identified a unidimensional measure of eHealth literacy scale (eHEALS). • EHEALS was not directly associated with depressive symptoms but positively associated with resilience. • Resilience plays the role of a full mediator in the relationship between eHEALS and depressive symptoms. • The indirect effect of eHEALS on depression through resilience was stronger among young adults than among adolescents. • Mental health information, and support delivered through digital tools could advance mental health with displaced youth.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".