Digital storytelling online: A case report exploring virtual design, implementation opportunities and challenges
Bibliographic record
Abstract
Abstract Background: Digital storytelling is an arts-informed approach that engages short, first-person videos, typically three to five minutes in length, to communicate a personal narrative. Prior to the pandemic, digital storytelling initiatives in health services research were often conducted during face-to-face workshops scheduled over multiple days. However, throughout the COVID-19 lockdowns where social distancing requirements needed to be maintained, many digital storytelling projects were adapted to online platforms. Methods: As part of a research project aiming to explore the day surgery treatment and recovery experiences of women with breast cancer in Peel region, we decided to pivot our digital storytelling process to an online format. During the process, we observed that the online digital storytelling format had multiple opportunities and challenges to implementation. Results: This paper outlines our promising practices and lessons learned when designing and implementing an online digital storytelling project including pre-production, production and post-production considerations. Conclusions: We provide lessons learned for future teams intending to conduct an online digital storytelling project.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| 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".