The Experience of Interprofessional Collaboration in a Telehealth Context in Primary Care: The Perspective of Patients Living with a Chronic Illness – A Research Protocol
Bibliographic record
Abstract
Abstract Background: The enhancement of primary health care and the prevalence of chronic diseases are key issues worldwide, especially in Canada. As the incidence of chronic illnesses rises, they have emerged as the foremost cause of mortality worldwide. This trend has led to a surge in demand for healthcare services, placing significant pressure on primary care systems. In 2019, with the arrival of the pandemic, the rapid introduction of telehealth emerged as a crucial resource for patients with chronic illnesses, augmenting the role of primary healthcare as their initial point of contact. This resource was implemented with no infrastructure, often without patient support, and left to the discretion of individual professionals. Interprofessional collaboration plays a critical role in optimizing the use of telehealth in managing chronic diseases. Interprofessional teams can provide comprehensive care that addresses the multifaceted needs of patients with chronic illnesses. This approach ensures that patients receive holistic and coordinated care, leading to better health outcomes. Despite its advantages, telehealth can have negative effects if used sub-optimally. Methods/design: To describe the process of interprofessional collaboration in the telehealth context in primary care coming from the perspective of patients living with chronic disease, this qualitative research is based on a constructivist research methodology, where the research team constructs knowledge derived from the interpretation of information obtained during the interviews with participants. To meet the study's objectives, qualitative Journey Mapping data collection will be carried out, following the approach of Trebbel. Individual interviews will be analyzed qualitatively and iteratively. This method is useful for analysis being done by different people from the team, including those with little experience in qualitative analysis. Anticipated benefits: The health and well-being of patients is central to the practice of healthcare professionals in primary care. Patients living with chronic diseases are among the most frequent users of primary care in Canada. The results of this study will support and improve the interprofessional collaboration process in the telehealth context, using a patient-centered approach. Journey mapping will help identify potential facilitating factors for improving primary care in the telehealth context according to the patient's journey. Results will be used to build a practical guide (phase 2) supporting interprofessional collaboration in the primary care telehealth context.
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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 | 0.033 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 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".