Using a Standardized Client Encounter to Practice Death Notification after the Unexpected Death of a Feline Patient Following Routine Ovariohysterectomy
Bibliographic record
Abstract
Death notification is an important skill for health care providers to carry out, yet few clinicians feel adequately prepared to complete this task. To address these gaps in clinical training, some medical educators have incorporated standardized patients (SPs) into the curriculum to allow students to practice death notification in a safe, controlled environment. Veterinary educators agree that end-of-life communication skills are essential for success in clinical practice, and many rely on standardized clients (SCs) for role-play concerning euthanasia. However, anticipatory loss is distinct from unexpected death, and death notification is strikingly absent from the veterinary literature. To introduce students to death notification, Midwestern University College of Veterinary Medicine (MWU CVM) developed a communications curriculum that culminated in a scripted encounter, “Basil, the Scottish Fold.” Students must explain to an SC that his kitten died following routine ovariohysterectomy. Pre- and post-event surveys completed by 19 students demonstrated valuable lessons in death notification word choice, particularly what not to say. I hope that this teaching tool may be adapted for use by other colleges of veterinary medicine to allow students to practice death notification.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".