Students' Experiences of Seeking Web-Based Animal Health Information at the Ontario Veterinary College: Exploratory Qualitative Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Although searching for health information on the internet has offered clear benefits of rapid access to information for seekers such as patients, medical practitioners, and students, detrimental effects on seekers’ experiences have also been documented. Health information overload is one such side effect, where an information seeker receives excessive volumes of potentially useful health-related messages that cannot be processed in a timely manner. This phenomenon has been documented among medical professionals, with consequences that include impacts on patient care. Presently, the use of the internet for health-related information, and particularly animal health information, in veterinary students has received far less research attention. OBJECTIVE The purpose of this study was to explore veterinary students’ internet search experiences to understand how students perceived the nature of Web-based information and how these perceptions influence their information management. METHODS For this qualitative exploratory study, 5 separate focus groups and a single interview were conducted between June and October 2016 with a sample of 21 veterinary students in Ontario, Canada. RESULTS Thematic analysis of focus group transcripts demonstrated one overarching theme, The Overwhelming Nature of the Internet, depicted by two subthemes: Volume and Type of Web-based Health Information and Processing, Managing, and Evaluating Information. CONCLUSIONS Integrating electronic health information literacy training into human health sciences students’ training has shown to have positive effects on information management skills. Given a recent Association of American Veterinary Medical Colleges report that considers health literacy as a professional competency, results of this study point to a direction for future research and for institutions to contemplate integrating information literacy skills in veterinary curricula. Specifically, we propose that the information literacy skills should include knowledge about access, retrieval, evaluation, and timely application of Web-based information.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".