Scoping review of inter-professional teamwork theories in health care: Implications for policy, practice and research
Bibliographic record
Abstract
As theoretical underpinning is a priority for inter-professional teamwork, this scoping review examines theories for inter-professional teamwork in health care in the academic literature over the last 10 years. The review found 56 papers from 4 databases (CINAHL, Medline, Scholar's Portal and Web of Science) published in English. A content strategy approach was used to categorise the theories, interventions and outcomes. The literature revealed a trend moving away from single theories, into multifaceted theories. There were more papers on inter-professional education interventions, compared to interprofessional practice or organisation interventions. Many papers reported the importance of patient outcomes as the driving force for teamwork. However, there is a lack of evidence to support this notion. Further research is suggested on teamwork effectiveness, including measurement and evaluation of patient and system outcomes. Based on the scoping literature review, a conceptual model is developed to align interventions with theory to outcomes, which considers four broad theoretical perspectives highlighted herein (learning, social-psychological, organisation and system), interventions at various levels (patient, profession, micro, meso and macro) and measurement of outcomes. This framework identifies various theories, interventions and outcomes, which will help direct policy, practice and research in matching the right theory(ies) to intervention(s) and outcome(s).
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 | 0.097 | 0.246 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.044 | 0.054 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".