Self-Efficacy and Student Satisfaction in a Clinical-Year Diagnostic Imaging Course Using an Online Instruction Format
Bibliographic record
Abstract
Abstract Accurate interpretation of radiographic images is critical to diagnosing clinical patients. Remote instruction in radiology has become more common at veterinary colleges as academic institutions struggle to fill open veterinary radiologist positions and as a result of the COVID-19 pandemic. This study sought to gather the feedback of fourth-year veterinary students via pre- and post-study surveys ( n = 45) and focus groups ( n = 7) about a newly implemented 2-week long radiology rotation. Ninety-eight percent of students reported having taken an online course before, and on both pre- and post-study surveys, students commonly reported feeling interested, determined, and attentive. On average, students reported that they were neither more nor less engaged than they would have been in an in-person course and that they understood the material neither better nor worse than they would have in an in-person course. Students reported that the key to their success was primarily hard work; secondarily, instructor availability and student ability were important. Students did not rate luck as having much influence on their success. Although diagnostic imaging can be a challenging subject to master, students effectively learned this subject through online instruction. They provided feedback for the course’s continued improvement; their comments centered around improved interactivity, including providing automated quiz questions’ answers and increased instructor availability. Data collected in this study will help to guide further development of the radiology course.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".