Academic Primer Series: Five Key Papers about Team Collaboration Relevant to Emergency Medicine
Bibliographic record
Abstract
Introduction: Team collaboration is an essential for success both within academics and the clinical environment. Often, team collaboration is not explicitly taught during medical school or even residency, and must be learned during one's early career. In this article, we aim to summarize five key papers about team collaboration for early career clinician educators. Methods: We conducted a consensus-building process among the writing team to generate a list of key papers that describe the importance or significance of team collaboration, seeking input from social media sources. The authors then used a three-round voting methodology akin to a Delphi study to determine the most important papers from the initially generated list. Results: The five most important papers on the topic of team collaboration, as determined by this mixed group of junior faculty members and faculty developers, are presented in this paper. For each included publication, a summary was provided along with its relevance to junior faculty members and faculty developers. Conclusion: Five key papers about team collaboration are presented in this publication. These papers provide a foundational background to help junior faculty members with collaborating in teams both clinically and academically. This list may also inform senior faculty and faculty developers about the needs of junior faculty members.
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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 | 0.049 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 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".