Health Care Practitioner Perceptions of Barriers and Enablers to Implementing Digital Health Technologies for Chronic Condition Self-Management in Primary Care Settings: A Scoping Review Protocol
Bibliographic record
Abstract
Digital health technologies can be beneficial for the self-management of chronic conditions (Allegrante et al., 2019; Grady & Gough, 2014); which are highly prevalent in Canada and worldwide (Government of Canada, 2023; Vos et al., 2020). A high proportion of individuals living with chronic conditions seek treatment in primary care settings (Palsson et al., 2020), and members of this population have more primary care encounters than people without chronic conditions (Queenan et al., 2021). Thus, health care providers working in primary care may be well positioned to encourage people living with chronic conditions to use digital health technologies for self-management of their condition. Identification of barriers and enablers of implementation is an important step in translating knowledge to action and developing theory-informed implementation interventions (French et al., 2012; Graham et al., 2006). Ideally, this should include identification of factors that can impact initial implementation and those that may impact sustained implementation over time (Zurynski et al., 2023). The views of health care providers on integrating digital health technologies for self-management of chronic conditions into clinical practice can provide valuable insight into how to best support initial and sustained implementation of these tools in primary care settings. This scoping review seeks to identify the perceived barriers and enablers of implementing and sustaining implementation of digital health technologies for the self-management of chronic conditions, as reported by health care providers in primary care settings.
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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 | 0.079 | 0.081 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.008 |
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".