Digital storytelling as a method in health research: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Digital storytelling is an arts-based research method with potential to elucidate complex narratives in a compelling manner, increase participant engagement, and enhance the meaning of research findings. This method involves the creation of a 3- to 5-min video that integrates multimedia materials including photos, participant voices, drawings, and music. Given the significant potential of digital storytelling to meaningfully capture and share participants' lived experiences, a systematic review of its use in healthcare research is crucial to develop an in-depth understanding of how researchers have used this method, with an aim to refine and further inform future iterations of its use. METHODS: We aim to identify and synthesize evidence on the use, impact, and ethical considerations of using digital storytelling in health research. The review questions are as follows: (1) What is known about the purpose, definition, use (processes), and contexts of digital storytelling as part of the research process in health research? (2) What impact does digital storytelling have upon the research process, knowledge development, and healthcare practice? (3) What are the key ethical considerations when using digital storytelling within qualitative, quantitative, and mixed method research studies? Key databases and the grey literature will be searched from 1990 to the present for qualitative, quantitative, and mixed methods studies that utilized digital storytelling as part of the research process. Two independent reviewers will screen and critically appraise relevant articles with established quality appraisal tools. We will extract narrative data from all studies with a standardized data extraction form and conduct a thematic analysis of the data. To facilitate innovative dissemination through social media, we will develop a visual infographic and three digital stories to illustrate the review findings, as well as methodological and ethical implications. DISCUSSION: In collaboration with national and international experts in digital storytelling, we will synthesize key evidence about digital storytelling that is critical to the development of methodological and ethical expertise about arts-based research methods. We will also develop recommendations for incorporating digital storytelling in a meaningful and ethical manner into the research process. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registry number 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 | 0.211 | 0.199 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.058 | 0.013 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".