Identifying optimal frameworks to implement or evaluate digital health interventions: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Digital health interventions (DHIs) are defined as health services delivered electronically through formal or informal care. DHIs can range from electronic medical records used by providers to mobile health apps used by consumers. DHIs involve complex interactions between user, technology and the healthcare team, posing challenges for implementation and evaluation. Theoretical or interpretive frameworks are crucial in providing researchers guidance and clarity on implementation or evaluation approaches; however, there is a lack of standardisation on which frameworks to use in which contexts. Our goal is to conduct a scoping review to identify frameworks to guide the implementation or evaluation of DHIs. METHODS AND ANALYSIS: A scoping review will be conducted using methods outlined by the Joanna Briggs Institute reviewers' manual and will conform to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. Studies will be included if they report on frameworks (ie, theoretical, interpretive, developmental) that are used to guide either implementation or evaluation of DHIs. Electronic databases, including MEDLINE, EMBASE, CINAHL and PsychINFO will be searched in addition to grey literature and reference lists of included studies. Citations and full text articles will be screened independently in Covidence after a reliability check among reviewers. We will use qualitative description to summarise findings and focus on how research objectives and type of DHIs are aligned with the frameworks used. ETHICS AND DISSEMINATION: We engaged an advisory panel of digital health knowledge users to provide input at strategic stages of the scoping review to enhance the relevance of findings and inform dissemination activities. Specifically, they will provide feedback on the eligibility criteria, data abstraction elements, interpretation of findings and assist in developing key messages for dissemination. This study does not require ethical review. Findings from review will support decision making when selecting appropriate frameworks to guide the implementation or evaluation of DHIs.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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; both teacher heads 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".