Understanding team dynamics to promote team building in a radiotherapy department
Bibliographic record
Abstract
Abstract Background: Teamwork is a central framework in healthcare delivery. Team dynamics can impact the team as a whole and has been identified within the literature as a contributory factor to quality and safety, patient satisfaction, staff satisfaction and overall performance. Within radiation therapy (RT), teamwork is essential in the delivery of high-quality care, yet team building and team development is under-reported. Aim: The focus of this research is to form a better understanding of what plays an impact on teams in a large urban RT cancer centre and how to better engage staff to work together, improve team dynamics and promote team building. Materials and Methods: An electronic search of the literature was conducted to better inform debate and aid in the development of team-building sessions in a busy radiotherapy department. Abstracts were screened and relevant articles selected if they met the search criteria that included relevancy related to team building, contributory factors on team dynamics, team-based learning, team performance and implication of civility. Results: A total of 45 articles were included in the final analysis. The majority were from the disciplines of medicine (45%), business (22%) and nursing (18%). Only 3 of the 45 articles (7%) focused on the profession of RT. Most articles discussed more than 1 theme with team dynamics and team building being the most common themes discussed in 16 articles each (36%). Other common themes included teamwork (31%), respect and civility (20%), leadership and hierarchy (11%), medical errors (11%) and team training (11%). Only 3 of the 45 articles (7%) focused on RT. Conclusion: There is a lack of longitudinal evidence to support the impact of team building sessions to improve team dynamics and promote a positive, cohesive team environment. Specifically within RT, the impact team building has on team dynamics has been under investigation. Highlights: High-quality patient care can be linked to team collaboration and cohesiveness. Changing the culture within a team and engaging in civility and respect in everyday practice has the potential to improve team dynamics, patient safety, staff and patient satisfaction.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".