Bibliographic record
Abstract
Abstract Interprofessional collaboration (IPC) is a cornerstone of today's healthcare organizations. IPC is intended to address fragmentation in healthcare systems and increasingly complex healthcare needs, which exceed the expertise of one profession. It requires that clinicians from different professions work together with patients, their families, carers, and communities to provide comprehensive services and quality care. Often linked to quality improvement initiatives, IPC is thought to enhance health outcomes, patient safety and satisfaction, and quality of care. Depending on the interdependence between collaborating clinicians, IPC practices can take various forms. The effectiveness of IPC depends on a set of interrelated factors involving healthcare systems and policy, healthcare organizations, interprofessional teams, and individuals. Communication is a key element of IPC effectiveness but remains undertheorized in much IPC literature. While IPC communication entails the accurate and timely transfer of information, it is also how clinicians negotiate and define patient care, and establish relationships with team members, patients, and their families. Health communication scholars can thus contribute nuanced understandings of IPC communicative practices. Future research should investigate the role of artifacts and especially the role of the person receiving IPC care.
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.
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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".