Responding To Vulnerable Patients With Multimorbidity: An Interprofessional Team Approach
Bibliographic record
Abstract
Abstract Background People with multimorbidity, who may be more vulnerable, often require care by an interprofessional primary healthcare (PHC) team that can tailor their approach to address the multiple and complex needs of this population. This study explored how the needs of vulnerable patients experiencing multimorbidity receive care by innovative interprofessional PHC teams during a one-hour consultation, outside of usual care. Methods This was a descriptive qualitative study. Forty-eight interviews were conducted with 20 allied healthcare professionals: (e.g., social work, pharmacy); 19 physicians (e.g., psychiatry, internal medicine, family medicine); and 9 decision makers. The thematic analysis was both iterative and interpretative using an individual and team approach to identify the main themes and exemplar quotations for illustration. Results Participants described patients with multimorbidity who were vulnerable as those experiencing major challenges accessing and navigating the healthcare system. Mental health issues were a major contributor to being vulnerable and often linked to common social determinants of health. Cultural factors were identified as potentially causing patients to be vulnerable. Participants articulated how the collaborative nature of the team generated new ideas and facilitated creative recommendations designed to meet the specific needs of each patient. Conclusions This one-time consultation went beyond the assessment of a patient’s multimorbidity by including a psycho-social-contextual understanding of vulnerability within the healthcare system. Findings may have important clinical and policy implications in the adoption and implementation of this approach and further assist vulnerable patients with multimorbidity.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".