Chronic Disease Patients’ Engagement in Interprofessional Telehealth Collaboration in Primary Care: A Scoping Review
Bibliographic record
Abstract
With the rise of people being affected with chronic illness, now the leading cause of mortality worldwide, primary care is overwhelmed with the demand for healthcare services. Primary healthcare is the first resource for patients living with chronic illness, but in 2019, COVID-19 brought healthcare professionals to increase services through virtual care for patients living with chronic illness. In the workplace, such professionals often need to be sufficiently resourced to collaborate, to address collaborative care barriers in telehealth and to keep patients engaged in their health. We performed a scoping review to identify how patients living with chronic diseases actively engage and describe their involvement in the process of interprofessional collaboration within the context of telehealth in primary care settings. We followed Arksey and O'Malley's and the Joanna Briggs Institute's methodological guidelines to conduct this scoping review. The analysis of the retained twelve studies showed little distinction between the experience of interprofessional collaboration from the patient's perspective in a telehealth context compared to a face-to-face context. However, we were able to identify gaps (eg, limited insight onto engagement dynamic, lack of patient-centric research, and insufficient research on patient engagement) relating to the experiences of patients, patient engagement, and professionals who have used telehealth. In an era of digital innovations, this lack of literature regarding the patient experience may jeopardize the quality of the interprofessional collaboration services offered to patients and patient engagement. This gap in patient engagement integrated into interprofessional collaboration in a telehealth context needs to be addressed.
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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 | 0.017 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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".