Digital Storytelling as a Method in Health Research: A Systematic Review
Bibliographic record
Abstract
Digital storytelling aims to illuminate complex narratives of health and illness when used as a method in health research. Digital stories are three to five minute videos that integrate written and narrated stories with multiple aesthetic components. There is increasing interest in digital storytelling as a research method, yet there is limited synthesized knowledge about its use. A systematic review to advance methodological understanding was warranted. Our systematic review purpose was to identify and synthesize evidence on the use, impact, and ethical considerations of digital storytelling as a method in health research. Key databases and online sources were searched for qualitative, quantitative, and mixed methods studies using digital storytelling. Articles with pediatric or adult populations, family members, or healthcare professionals were included. The focus was on digital storytelling in health research, where it was used as a method, at any point in the research process. Two independent reviewers screened abstracts and full texts to confirm eligibility. We conducted a narrative synthesis of the extracted narrative data. The searches yielded 7285 articles. Following the removal of duplicates and screening, 46 articles met the inclusion criteria, which predominantly used qualitative methodology. An analysis of the extracted data resulted in seven descriptive themes which provided insight into the purpose, definition, process, context, impact and ethical considerations of this method. Digital storytelling is an empowering and disruptive method that captures voice through a process-oriented, flexible approach. It is particularly effective at honouring local and cultural knowledge, and evoking change. Researchers have used consistent facilitation approaches, but theoretical inconsistency, diverse positioning in analysis, and ethical complexity remain significant challenges. These findings provide methodological insight for applying digital storytelling in future research. Systematic review protocol registration: CRD42017068002.
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 | 0.053 | 0.199 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".