Assessment of Post-Operative Pain in Cats: A Case Study on Veterinary Students of Universiti Putra Malaysia
Bibliographic record
Abstract
The ability to assess and control pain is listed as one of the desired Day One competencies among veterinary graduates. As such, a study was conducted to examine the current status and effectiveness of a video-based training module on the attitude toward and knowledge of pain assessment in cats among fourth- and final-year veterinary students of Universiti Putra Malaysia (UPM) in January of 2013. A total of 92 students participated in this study, resulting in a response rate of 60.1%. Upon completion of a pre-training survey, the respondents undertook an interactive video-based presentation, followed by a post-training survey. The majority of the students (96.7%) agreed on the importance of pain management. Before the training, many (76.1%) disagreed that they had received adequate training, while 53.3% were not confident in their pain-recognition skills. After training, their knowledge and confidence in pain assessment increased. Responses to the survey were not associated with differences in gender, level of study, or field of interest. Students were found to have mistaken some physiologic parameters as good pain indicators after ovariohysterectomy. Their assessment of three standardized video cases revealed that they could recognize prominent signs of pain but failed to identify changes in behavior that were more subtle. Refinement to the training module is required to address the above deficiencies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".