Healthcare teams and patient‐related terminology: a review of concepts and uses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it