The Application of Information Technology in the Teaching of Veterinary Epidemiology and Public Health
Bibliographic record
Abstract
Information technology (IT) is an imprecise term currently used to describe computer-based techniques for data manipulation, storage, dissemination, publication, and retrieval. IT possesses several characteristics that promote meaningful learning, including (1) just-in-time, personalized; (2) student-centered versus teacher-centric; (3) self-paced; (4) anytime, anywhere; and (5) discovery (through bibliographic and other information searches). However, if done improperly, IT-based teaching can be counterproductive. Factors to consider when evaluating the effectiveness of IT-based teaching methods include (1) content, (2) learning, (3) delivery support, (4) usability, and (5) technological. IT has been used to support instruction in epidemiology and public health at many levels, ranging from basic computer literacy to hands-on training in epidemiological methods through computer-based problem sets, case workups, outbreak investigations, and tutorials. Online quizzes based on articles selected from practice-oriented journals have been used to promote evidence-based medicine skills, including the critical evaluation of medical claims. As online access and delivery improve, opportunities for substantive online education and lifelong learning through IT have expanded. One of the most novel and comprehensive implementations of collaborative online sharing of educational content in epidemiology and public health is the Epidemiology Supercourse (http://www.pitt.edu/~super1/). More than 9,000 faculty from 118 countries have contributed to an online library of more than 700 lectures with quality control and adherence to accepted pedagogic principles. The goal is to improve teaching and research in epidemiology and public health worldwide. Although the focus is on human medicine, the concepts, methods, and principles can easily be applied to veterinary medicine. The Association for Veterinary Epidemiology and Preventive Medicine (AVEPM) seeks to heighten awareness of issues in veterinary epidemiology and public health education among veterinary educators through various forums, symposia, and workshops. The AVEPM Web site (http://www.cvm.uiuc.edu/avepm/) includes a listing of educational software and Web sites supporting epidemiology and public health education.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".