Lessons Learned from a Pilot Study Implementing a Team-Based Messaging Application (Slack) to Improve Communication and Teamwork in Veterinary Medical Education
Bibliographic record
Abstract
Communication skills are paramount for a successful veterinary curriculum. Technological advances have improved communication processes, and the way instruction is delivered. Yet, with these advances come challenges such as email overload, increased interruptions, and miscommunications. Time is a valuable commodity at any high caseload veterinary teaching hospital. When increasingly more time is spent sending and receiving emails, text messages, pages, and calls in lieu of more focused clinical teaching, then the modes of communication and traditional learning theories need to be evaluated. An effective mode of communication is needed to reduce information overload and miscommunication. This article describes lessons learned from a pilot study to determine if a team-based messaging application could improve a surgical team's communication by having all forms of transmitted media directly related to their scope of work accessible to everyone on the team in one real-time digital platform (Slack). Fifteen members of a university-based surgical team were enrolled into the study and provided with surveys at specific time points to evaluate the efficacy of an internet-based team communication tool during a 3-month period. Results of our study showed an overall perception of improved communication among team members when using a team-based communication platform. Recommendations are provided to address team member's underutilization of the platform, which resulted in duplicate messages and miscommunication. We conclude an initial adoption by staff members is essential when implementing significant shifts in communication platforms.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".