Digital storytelling in health professions education: a systematic review
Bibliographic record
Abstract
BACKGROUND: Digital stories are short videos that combine stand-alone and first-person narratives with multimedia. This systematic review examined the contexts and purposes for using digital storytelling in health professions education (HPE) as well as its impact on health professionals' learning and behaviours. METHODS: We focused on the results of HPE studies gleaned from a larger systematic review that explored digital storytelling in healthcare and HPE. In December 2016, we searched MEDLINE, EMBASE, PsycINFO, CINAHL, and ERIC. We included all English-language studies on digital storytelling that reported at least one outcome from Levels 2 (learning) or 3 (behaviour) of The New World Kirkpatrick Model. Two reviewers independently screened articles for inclusion and extracted data. RESULTS: The comprehensive search (i.e., digital storytelling in healthcare and HPE) resulted in 1486 unique titles/abstracts. Of these, 153 were eligible for full review and 42 pertained to HPE. Sixteen HPE articles were suitable for data extraction; 14 focused on health professionals' learning and two investigated health professionals' learning as well as their behaviour changes. Half represented the undergraduate nursing context. The purposes for using digital storytelling were eclectic. The co-creation of patients' digital stories with health professionals as well as the creation and use of health professionals' own digital stories enhanced learning. Patients' digital stories alone had minimal impact on health professionals' learning. CONCLUSIONS: This review highlights the need for high-quality research on the impact of digital storytelling in HPE, especially on health professionals' behaviours. PROSPERO REGISTRATION NUMBER: CRD42016050271 .
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".