Enhancing patient-engaged teamwork in healthcare: an observational case study
Bibliographic record
Abstract
The purpose of this study was to describe how teamwork that effectively engaged patients and families, manifested itself in an acute rural care setting in order to inform the development of teamwork skills. One hundred and forty participants were included in the study representing providers, patients, family, hospital and clinical support personnel, education specialists and students. Using a modified grounded theory approach, and informed by activity theory, observational field notes and interview transcripts were analyzed. Through the analysis of 343 events of providers interacting with, or exchanging information about, patients, three patterns of teamwork emerged that facilitated patient-engaged care: uniprofessional, multiprofessional and interprofessional. The data indicated that providers navigated between these patterns, as well as others, throughout their workday. Providers should be skilled in applying the construct of situation awareness in order to adopt a pattern of teamwork that best facilitates patient-engaged care. Interventions that can enhance teamwork should focus on: valuing the perspectives of others; developing relational competence and resilience; employing reflective learning and shared decision-making skills; and incorporating principles of change theory for both individuals and systems.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".