Boundaries, gaps, and overlaps: defining roles in a multidisciplinary nephrology clinic
Bibliographic record
Abstract
Terese Stenfors-Hayes,1 Helen H Kang2 1Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden; 2BC Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada Abstract: This study aims to explore how health care professionals in a multidisciplinary chronic kidney disease clinic interact with one another, patients, families, and caregivers to expand understanding of how this increasingly common form of chronic disease management functions in situ. Nonparticipatory observations were conducted of 64 consultations between patients and health care professionals and end-of-day rounds at a multidisciplinary chronic kidney disease clinic. Key themes in our findings revolved around the question of boundaries between the health professions that were expected to work cooperatively within the clinic, between medical specialties in the management of complex patients, and between caregivers and patients. Understanding the importance of various professional roles and how they are allocated, either formally as part of care design or organically as a clinical routine, may help us understand how multidisciplinary care teams function in real life and help us identify gaps in practice. This study highlights two areas for further study and reflection: the effect of discrepancies in health information and the role of caregivers in patient care. Keywords: clinical medicine, interprofessional relations, observation, qualitative research
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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 | 0.049 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.026 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.003 | 0.006 |
| 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".