Trauma-Informed Care in Digital Health Technologies: Protocol for a Scoping Review (Preprint)
Bibliographic record
Abstract
BACKGROUND The use of digital health technologies is becoming increasingly common across the globe as they offer immense potential to enhance health care delivery by promoting accessibility, flexibility, and personalized care, connecting patients to health care professionals, and offering more efficient services and treatments to remote residents. At the same time, there is an increasing recognition of how digital health can inadvertently foment psychological trauma. This phenomenon has led to the adoption of trauma-informed care in designing and deploying digital health technologies. However, how trauma-informed care is defined and characterized, and the various trauma-informed care strategies used in designing and deploying digital health technologies remain unexplored. OBJECTIVE This scoping review aims to explore and synthesize the literature on how trauma-informed care is defined and characterized in digital health and the various trauma-informed care principles, strategies, or recommendations used in designing and deploying digital health. METHODS This review will draw on the Joanna Briggs Institute’s updated methodological guidance for scoping reviews. A search will be conducted on CINAHL, PubMed, Embase, Compendex Engineering Village, Web of Science, Scopus, and PsycINFO. This review will consider published research studies and unpublished work (gray literature). Studies will be included if they applied trauma-informed care in designing or deploying digital health for patients across all geographical locations or provide trauma-informed recommendations on how web developers should develop digital health. Studies will be limited to publications within the past 10 years and studies in all languages will be considered. Two independent reviewers will screen the titles and abstracts, and then perform a full-text review. Data will be extracted into a data extraction tool developed for this study. RESULTS The scoping review was undergoing a full search as of April 2023. The main results will synthesize the peer-reviewed and gray literature on adopting trauma-informed care practices in digital health research and development. The study is expected to be completed by December 2023 and the results are expected to be published in a peer-reviewed journal. CONCLUSIONS This review is expected to provide the knowledge base on the adoption of trauma-informed care in designing and deploying digital health. This knowledge can lead to more engaging, and likely, more effective digital health interventions that have less potential for harm. A synthesis of the various trauma-informed care strategies in digital health will also provide a trauma-informed language by enabling researchers and digital health developers to consider trauma as a critical factor in each stage of the design process. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/46842
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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 | 0.095 | 0.120 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.123 | 0.023 |
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".