Healthcare teams and patient‐related terminology: a review of concepts and uses
Bibliographic record
Abstract
BACKGROUND: Discussions concerning health care teams and patient-related terminology remain an ongoing debate. Terms such as interdisciplinary, multidisciplinary and transdisciplinary, as well as interprofessional are ambiguously defined and frequently used, rightly or wrongly, interchangeably. Also, clarification on the terminology regarding patients is rarely explicitly addressed in the health care team's literature, potentially resulting in confusion among health professional students, novice researchers, and practitioners. METHODS: A structured literature review was conducted. Electronic searches were performed from August 2018 to September 2019 on the following databases: CINHAL, Scopus, Science Direct, PubMed, Nursing and Allied Health and JSTOR. The following terms were used: 'terminology', 'team(s)', 'nursing', 'health', 'medical', 'education', 'interprofessional', 'interdisciplinary', 'multidisciplinary', 'transdisciplinary', 'collaboration', 'patient', 'client', 'customer', 'user' and 'person'. RESULTS: Small but significant nuances in the use of language and its implications for patient care can be made visible for health professional education and clinical practice. Healthcare is necessarily interdisciplinary and therefore we are obligated, and privileged, to think more critically about the use of terminology to ensure we are supporting high-quality evidence and knowledge application. CONCLUSION: To avoid confusion and lack of consistency in the peer-review literature, authors should be encouraged to offer brief definitions and the rationale for the use of a particular term or group of term. In addition, a deeper understanding of the values that each patient-related term represents for particular disciplines or health care professions is essential to achieve a more comprehensive conceptual rigour.
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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 | 0.026 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.029 | 0.033 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".