Assessment and Revision of the Veterinary Internship and Residency Matching Program Standardized Letter of Reference
Bibliographic record
Abstract
The Veterinary Internship and Residency Matching Program (VIRMP) recently revised its electronic standardized letter of reference (SLOR) to improve the quality and usefulness of the data obtained from it and to enhance the relevance of non-cognitive and cognitive candidate attributes assessed. We used a stepwise process including a broad survey of SLOR readers and writers, analysis of past SLORs, and a multi-wave iterative revision that included key stakeholders, such as residency and internship program directors from academia and private practice. Data from the SLOR survey and analysis of past SLOR responses identified opportunities to improve applicant differentiation, mitigate positive bias, and encourage response consistency. The survey and other analytics identified and confirmed performance domains of high relevance. The revised SLOR assesses four performance domains: knowledge base and clinical skills, stress and time management, interpersonal skills, and personal characteristics. Ratings within the revised SLOR are predominantly criterion-referenced to enhance discernment of candidate attributes contained within each domain. Questions assessing areas of strength and targeted mentoring were replaced with free-text boxes, which allow writers to comment on positive and neutral/negative ratings of attributes within domains. Minor revisions were made to certain questions to enhance readability, streamline responses, or address targeted concerns identified in the SLOR survey or stakeholder review. The revised SLOR was deployed in the 2020 VIRMP; data from a survey of writers ( n = 647) and readers ( n = 378) indicate that the redesign objectives were achieved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.095 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".