The need for clarification of terminology and labels in interprofessional care: A commentary
Bibliographic record
Abstract
Background: The current healthcare environment is filled with numerous team caring models, which are often used interchangeably, but ultimately mean different levels of collaboration among HCPs, and between HCPs and patients: multiprofessional collaboration, transprofessional collaboration, and interprofessional patient-centered collaborative (IPCC) care. Furthermore, the labels for these care models are not patient-friendly, portraying that only HCP ‘professionals’ comprise the team membership. Clarity is required around the terminology and labeling of these caring models to ensure enhanced patient involvement within interprofessional teams. Discussion: The definitions of the three team care models are provided with an explanation of how these models of care connect to the 55-year-old patient’s case and impact on the relationship between HCPs and patient. Conclusion: While IPCC care is considered as the gold standard for the collaboration between a variety of HCP professional groups and the patient, work needs to be done on the label applied to this caring model. Future research should explore, from patients’ perspectives, the labels used in IPCC care and propose an alternative title that is more inclusive of patients as team members.
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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 | 0.124 | 0.347 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.012 | 0.040 |
| Scholarly communication | 0.016 | 0.037 |
| Open science | 0.017 | 0.014 |
| Research integrity | 0.069 | 0.118 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".