Implementing a Dissection‐based Human Anatomy Laboratory Manual: An Assessment of Students' Use, Perceived Usefulness, and Resulting Laboratory Preparedness
Bibliographic record
Abstract
The University of Guelph provides year‐round dissection‐ and prosection‐based human anatomy courses to undergraduate students; the combined enrolment in laboratory‐based courses exceeds 1000 students per academic year. Given such large enrolment numbers, an ongoing challenge is to continue to provide a very high level of human anatomy education to students with current resources. Consequently, various educational tools are being developed to enhance the current human anatomy program. To specifically target the course and laboratory learning objectives, a dissection‐based human anatomy laboratory manual has been created and is under further development, with the goal of promoting student preparedness and self‐sufficiency in the laboratory. Detailed cadaveric, dissection‐based images with clear and simple dissection instructions are the fundamental qualities of the manual. This resource also incorporates active learning exercises including drawing, colouring, and labelling components. A pilot edition of the manual was released to students enrolled in the third‐year dissection‐ and prosection‐based courses in the fall semester of 2017 (HK*3401 and HK*3501). Data pertaining to the students' use and perceived usefulness of the manual, as well as their perceived preparedness for laboratory periods have been collected. Analysis of these data is ongoing, and will be used to gain insight for the future development of subsequent editions of the manual. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".