Digital health technology adoption factors: a rapid review of systematic reviews and checklist development
Bibliographic record
Abstract
Objective This rapid review of systematic reviews aimed to synthesize the adoption factors of digital health technologies (DHTs).Methods A reference search was performed on MEDLINE, EMBASE, CINAHL, PsychInfo, PubMed, EBM Review, Web of Science, Scopus, PROSPERO and Google Scholar to search systematic reviews published in the last three years. Study selection was conducted following the PRISMA guidelines. The methodological quality of the included systematic reviews was assessed with the AMSTAR 2 tool. The identified adoption factors were classified using the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework, in which the self-determination factors were integrated.Results Out of 4277 identified references, 45 systematic reviews were retained. The quality of most included systematic reviews, assessed by AMSTAR 2, was critically low. The most cited adoption factors included DHT’s ease of use, training for using DHT, adopters’ access to DHT and a high-speed internet connection, technical support, DHT’s customizability, the relevance and reliability of DHT data, the demand-side value of DHT (desirability), safety, cost-effectiveness, staff and patient competence, patient relatedness with others, organizational readiness, necessary changes in team routines, capacity for innovation, along with the political, economic, regulatory and sociocultural contexts. Integrating the findings of this rapid review, a DHT adoption checklist was elaborated. This checklist would aid future developers and implementers of DHT in successfully adopting the technology.Conclusions This review synthesized the DHT adoption factors using the NASSS framework and Self-Determination Theory. When developing or implementing a DHT, the micro-, meso- and macro-level adoption factors must be considered.
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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 | 0.202 | 0.370 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.064 | 0.042 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".