Survey of Institutional Teaching Approaches to Clinical-Year Clinical Pathology Instruction and Comparison with Prior Survey Results
Bibliographic record
Abstract
Teaching approaches to veterinary clinical pathology in the final (clinical) year of veterinary school are often different than those for other specialties. Anecdotally, many schools teach these rotations separately from the routine diagnostic service, but minimal published data are available on this topic or on approaches to teaching and assessment in these rotations. An online survey of 69 veterinary institutions around the world was conducted in 2019. A total of 30 completed surveys were received from 10 countries; 22 completed responses were from North American institutions (73.3%). Survey question categories included information on basic rotations, including microscopy format, personnel involved in instruction, and assessment methods; information on advanced rotations; and challenges and successes with clinical pathology instruction. Data were analyzed and, when appropriate, compared with results from a similar survey conducted in 1997. Formats and content varied greatly among institutions. Several shifts in teaching strategies and rotation format over time were found since the 1997 survey, including increased use of projection microscopy and decreased use of multiheaded microscopy in 2019. More teaching by medical technologists and residents, less teaching by faculty, and a significant increase in the number of students per rotation were seen in 2019 compared with 1997. Several free-text comments referred to challenges related to increasing class size. These data and the comparison with the prior survey highlight common challenges and potential solutions to final-year clinical pathology instruction. Creation of specific, measurable objectives for clinical pathology competence may aid future development and refinement of clinical pathology teaching.
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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".