Barriers and facilitators to patient-to-provider messaging using the COM-B model and theoretical domains framework: a rapid umbrella review
Bibliographic record
Abstract
Abstract Background Virtual patient-to-provider messaging systems such as text messaging have the potential to improve healthcare access; however, little research has used theory to understand the barriers and facilitators impacting uptake of these systems by patients and healthcare providers. This review uses the Capability-Opportunity-Motivation-Behaviour (COM-B) model and the Theoretical Domains Framework (TDF) to explore barriers and facilitators of patient-to-provider messaging. Methods A rapid umbrella review method was followed. Medline and CINAHL were searched for review articles that examined patient-to-provider implementation barriers and facilitators by patients or healthcare providers. Two coders extracted implementation barriers and facilitators, and one coder mapped these barriers and facilitators on to the COM-B and TDF. Results Fifty-nine unique barriers and facilitators were extracted. Regarding healthcare provider oriented barriers and facilitators, the most frequently identified COM-B components included Reflective Motivation (identified in 42% of provider barriers and facilitators), Psychological Capability (19%) and Physical Opportunity (19%) and TDF domains included Beliefs about Consequences (identified in 28% of provider barriers and facilitators), Environmental Context and Resources (19%), and Social Influences (17%). Regarding patient oriented barriers and facilitators, the most frequently identified COM-B components included Reflective Motivation (identified in 55% of patient barriers and facilitators), Psychological Capability (16%), and Physical Opportunity (16%) and TDF domains included Beliefs about Consequences (identified in 30% of patient barriers and facilitators), Environmental Context and Resources (16%), and Beliefs about Capabilities (11%). Conclusions Both patients and healthcare providers experience barriers to implementing patient-to-provider messaging systems. By conducting a COM-B and TDF-based analysis of the implementation barriers and facilitators, this review highlights several theoretical domains for researchers, healthcare systems, and policy-makers to focus on when designing interventions that can effectively target these issues and enhance the impact and reach of virtual messaging systems in the future.
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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 | 0.027 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".