Implementing Digital Storytelling for Health-Related Outcomes in Older Adults: Protocol for a Systematic Review
Bibliographic record
Abstract
BACKGROUND: The number of older adults is increasing rapidly worldwide. Older adults face a unique set of challenges and may experience a range of psychological comorbidities. Advances in multimedia technology have allowed for digital storytelling to be utilized as an intervention for health-related outcomes. OBJECTIVE: The primary aim of the proposed systematic review is to examine the reported health-related outcomes for older adults engaged in digital storytelling. The review also aims to examine the methods associated with digital storytelling, characteristics of digital story products, and implementational considerations. METHODS: This protocol adheres to the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols. We will systematically search selected electronic databases to identify studies that meet our eligibility criteria. From the included studies, data will be extracted and synthesized using a narrative approach and summarized in tables. The methodological quality of the included studies will be assessed using the Mixed Methods Appraisal Tool. RESULTS: Systematic searches, data extraction and analysis, and writing of the systematic review are expected to be completed by the end of 2019. CONCLUSIONS: The proposed systematic review will summarize the existing studies using digital storytelling to improve health-related outcomes for older adults. Results from this review will provide an evidence base for the development of digital storytelling interventions that are effective and implementable with older adults. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/15512.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".