Personal health and nutrition information-seeking attitudes and behaviours of first year Canadian and United States veterinary students
Bibliographic record
Abstract
Objective: To identify the primary sources of information first year Canadian and US veterinary students relied on for their personal health and nutrition information, and to explore their attitudes towards, and perceptions of, health information resources. Background: Though the animal health information-seeking behaviours (HISB) of veterinary students have been explored, research regarding personal HISB of this professional student population is limited. Evidentiary value: Participants were first year veterinary students (n=322) at the five Canadian veterinary schools and five randomly selected US veterinary schools. An online questionnaire was used to gather students' demographic information, sources of health and nutrition information, and information-seeking attitudes and perceptions. This study may impact practice at the institutional level for veterinary educators. Methods: was used for quantitative analysis; involving multivariate logistic regression models, univariate analyses, and measures of frequency. Results: Results indicated high reliance on the Internet for personal health 213/322 (66%) and nutrition 196/322 (61%) information. While respondents revealed high trust levels in dietary recommendations from family doctors, 132/322 (41%) of students revealed their doctor did not provide any information on healthy diets. Students who reported the use of peer-reviewed journal articles for personal nutrition information were at greater odds of having confidence in knowing where to find nutrition information (Odds Ratio [OR] = 6.61, p<0.001). Conclusion: Participating students reported a high reliance on the Internet search engine Google, and a general lack of guidance from medical professionals regarding general health needs. Application: Veterinary schools should consider this information to enhance student information literacy skills, particularly to facilitate personal HISB, and consequently help in management of personal health throughout the growing demands of the programme.
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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 | 0.003 |
| 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 | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".