An Interactive Teddy Bear Clinic Tour: Teaching Veterinary Students How to Interact with Young Children
Bibliographic record
Abstract
Although there are existing guidelines for teaching and learning skillful client communication, there remains a need to integrate a developmental focus into veterinary medical curricula to prepare students for interactions with children who accompany their companion animals. The objectives of this teaching tip are (1) to describe the use of a Teddy Bear Clinic Tour as an innovative, applied practice method for teaching veterinary students about clinical communication with children, and (2) to provide accompanying resources to enable use of this method to teach clinical communication at other facilities. This paper includes practical guidelines for organizing a Teddy Bear Clinic Tour at training clinics or colleges of veterinary medicine; an anecdotal description of a pilot study at the Ontario Veterinary College Smith Lane Animal Hospital; and printable resources, including a list of specific clinical communication skills, a sample evaluation sheet for supervisors and students, recommendations for creating a child-friendly environment, examples of child-friendly veterinary vocabulary, and a sample script for a Teddy Bear Clinic Tour. Informed by the resources provided in this teaching tip paper, the Teddy Bear Clinic Tour can be used at your facility as a unique teaching method for clinical communication with children and as a community outreach program to advertise the services at the facility.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".