Effective Teamwork in Healthcare: Research and Reality
Bibliographic record
Abstract
Issues affecting health workplaces range from serious concerns that could affect the immediate physical safety of workers to those that would improve productivity and efficiency, or make an organization a preferred employer. Employers and workers might consider effective teamwork an asset, but for patients it is a prerequisite. This paper reviews the evidence for effective teamwork, primarily that gathered by a research team funded by the Canadian Health Services Research Foundation (CHSRF). We also review the expert opinion provided by a group of 25 researchers and decision makers convened by CHSRF in late 2005 at a forum for discussion about issues related to effective teamwork. Included in the retreat were representatives from professional organizations and occupations as well as areas such as legal liability. Taken together, the research and expert opinion provide a comprehensive overview of the benefits of effective teamwork and the conditions needed for its implementation. In addition, we review policy and management perspectives on the most significant challenges to the implementation of effective teamwork in the Canadian context, and potential opportunities to overcome these obstacles.
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 | 0.058 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".