Words, Camera, Music, Action: A Methodology of Digital Storytelling in a Health Care Setting
Bibliographic record
Abstract
In this technological age, storytelling is moving from oral and written to digital formats, creating many methodological opportunities for researchers and practitioners. This article explores a specific genre of participatory media production, digital storytelling (DST), which could be a valuable research tool to describe, analyze, and understand the experiences of research participants. Digital stories (DS) are short movies that use images, videos, a voice-over, and various video editing techniques to share an important story from the participant’s life. In a health care setting, DS can be used as knowledge translation tools for education and advocacy, as data to be analyzed in the research process, or as a therapeutic intervention, in any combination, depending on the intent of the project. Although an increasing number of health-related research studies indicate using DST, or some variation of it, there is a glaring paucity of methodologically focused manuscripts in the health care literature. This article delineates and describes four primary phases of DST in a health care context as finding the story, telling the story, crafting the story, and sharing the story. Both the creative and technical considerations of DST facilitation are elucidated through specific examples and practical concepts. By drawing from diverse literature such as narratology, film, and psychotherapy, and exploring new creative tools and ideas to help research participants convey meaning, this article provides a starting point for qualitative researchers to explore the use of DST in their own contexts.
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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 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".