Feature Story: Social Work grad makes her mark at Veterinary College
Bibliographic record
Abstract
On a recent veterinary call to a nearby farm, social worker Erin Wasson found herself pushing a sheep through a chute. “Part of the job is to actually help,” says Wasson, the Western College of Veterinary Medicine’s (WCVM) first social worker and a graduate of the Faculty of Social Work at the U of R. Wasson is part of the Veterinary Social Work Initiative — the first of its kind in Canada. It’s a groundbreaking program that provides social work support to a range of people at the regional veterinary college and its veterinary medical centre: animal owners, clinical faculty and staff, and veterinary students. Wasson is there to ensure people get the support they need. Her days can be as varied as counselling an overworked clinician or helping a family deal with the death of a beloved pet. She’s been called in to help manage cases that include incidences of traumatic grief, sudden deaths, end-of-life decision-making and even domestic violence.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.124 | 0.025 |
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".