The role of digital storytelling methods in promoting health-related outcomes among young adults of color: a systematic review
Bibliographic record
Abstract
Background: Digital storytelling (DST) encourages participants to use technology to create narratives and share personal experiences. Although DST can amplify the voices of groups experiencing marginalization, it is unclear how DST has been used among young adults of color. We also lack a comprehensive understanding of DST's role in addressing health-related outcomes. We conducted a systematic review to examine the role of DST in influencing health-related knowledge, attitudes, and behaviors among young adults of color. Methods: We searched three databases for peer-reviewed literature between January 1, 2000, through December 31, 2023, for references examining DST methods among young adults (aged 18-39 years) of color that included at least one health-related outcome. We used a two-stage review process to assess eligibility. Data were analyzed by DST type and health outcome. Results: From 877 references, we identified 33 eligible articles, most of which were from Canada and the USA. Studies addressed infectious diseases (n=13), including human immunodeficiency virus (HIV); mental health issues (n=10); maternal and reproductive health (n=5); and behavioral or other health themes (n=5). Across health areas, studies were divided by DST type: media creation (e.g., participants created digital stories) or media consumption (e.g., participants or other community members were exposed to stories). Studies using media creation often use qualitative methods to identify social determinants of health or reveal complex health-related issues. Studies using media consumption often used quantitative methods to measure knowledge, attitudes, and behaviors. Across health topics, DST demonstrated some effect in changing health knowledge and attitudes, but mixed results in terms of shifting health behaviors. Conclusions: DST can amplify the voices of young adults of color, especially regarding complex issues, such as managing HIV or mental health issues. Media creation studies explored lived experiences, while media consumption studies showed that DST was effective at improving knowledge and attitudes related to certain health issues. More randomized controlled trials may be necessary to better assess the effect of DST interventions on modifying health behaviors. Researchers should also consider the value of DST beyond health outcomes intended to be measured, including the ability to empower young adults of color to be more fully engaged throughout the research process.
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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 | 0.015 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".