Veterinarians' information Prescription and Clients' eHealth Literacy
Bibliographic record
Abstract
Introduction: The aim of this study is to investigate the relationship between pet owner’s combined knowledge, comfort, and perceived skills at finding, evaluating, applying online pet health information, and the application of the information prescription (IP) provided for pet owners education on the internet. Methods: Thirty telephone interviews were conducted followed by a questionnaire of eHealth Literacy Scale (eHEALS) with pet owners after receiving an IP with a suggested websites in addition to their customary veterinary services in a vet clinic at the center of Tehran, Iran. Qualitative and quantitative data were merged to explore differences and similarities among respondents with different eHealth literacy levels. Results: Results indicate that pet owners with higher score of eHealth literacy more accessed the suggested websites and reported positive feelings about this addition to their veterinary services. Similarly, among the eight-item self-reported eHealth Literacy skills, perceived skills at evaluating and applying, were significantly associated with the use of IPs. Lastly eHealth literacy level was significantly associated with the outcomes of prescribed information, such as veterinarians-client communication outcome and learning outcomes. Conclusion: Disparities in application of the veterinarian’s IPs for online pet healthcare information, and its outcomes are associated with different eHealth literacy skills. Veterinarians should collaborate with information specialists and librarians to perform education efforts to raise awareness on online pet health information quality and impact of veterinarian directed information prescription especially among low health literate owners.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".