Perspectives of specialists and family physicians in interprofessional teams in caring for patients with multimorbidity: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Patients with multimorbidity often require services across different health care settings, yet team processes among settings are rarely implemented. We explored perceptions of specialists and family physicians collaborating in a telemedicine interprofessional consultation for patients with multimorbidity to better understand the value of bringing physicians together across the boundaries of health care settings. METHODS: This was a descriptive qualitative, interview-based study. Physicians who had previously participated in the Telemedicine Interprofessional Model of Practice for Aging and Complex Treatments (Telemedicine IMPACT Plus [TIP] Program) were invited to participate and asked to describe their experience of being a member of the program. Interviews were conducted from March to May 2016. We conducted an iterative and interpretive process using both individual and team analysis to identify themes. RESULTS: There were 15 participants, 9 specialists and 6 family physicians. Three themes emerged in the analysis: creating new perspectives on care for patients with multimorbidity by sharing knowledge, skills and attitudes; the shift from a consultant model to an interprofessional team model (allowing a window into the community, extending discussions beyond the medical model and focusing on the patient's health in context); and opportunities for learners, including learning about interprofessional collaboration and gaining exposure to a real-world model for caring for people with multimorbidity in outpatient settings. INTERPRETATION: Family physicians and specialists participating in a TIP Program believed the program improved their knowledge and skills, while also serving as an effective care delivery strategy. The findings also support that learners require more exposure to nontraditional consultant models in order to care for patients with multimorbidity effectively.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".