Pet owners' online information searches and the perceived effects on interactions and relationships with their veterinarians
Bibliographic record
Abstract
Objective: To explore pet owners’ online search experiences for pet health information and the perceived effects on their interactions and relationships with veterinarians. Background: Few studies have examined pet owners’ online searches for pet health information; even less is known about how these search experiences may impact pet owners’ interactions and relationships with veterinarians, including any effects on bond-centered care. Methods: Qualitative study consisting of five focus groups conducted with 26 pet owners in the Greater Toronto Area, Ontario, Canada, between June to September 2016. All interviews were audio-recorded and transcribed verbatim. QSR NVivo 11® was used to facilitate organisation of focus group data for thematic analysis. Results: Participating pet owners frequently referred to their relationships with veterinarians when discussing experiences searching online for pet health information. Owners reported choosing either to disclose or withhold declaring their online searches to veterinarians, depending on whether participants perceived a beneficial or detrimental impact on a “good” professional relationship with their veterinarian. Perceptions of veterinarians' reactions towards declaration of online searches were mixed, and influenced pet owners’ views of the existing relationship. Conclusion: Pet owners viewed their veterinarians as their most trusted source of pet health information, but many owners also wanted supplemental information from online searches. Owners preferred veterinarians refer them to online pet health resources, ideally those affiliated with the veterinary profession. Searching for pet health information online does not displace veterinarians’ guidance. Rather, the veterinarian-owner relationship was perceived to be strengthened when online searches were openly discussed with veterinarians. Implications: Findings offer insight into pet owners’ expectations of veterinarians within the context of online pet health information, providing ideas for veterinarians to strengthen bonds with owners such as; showing support of owners’ online pet health information searching by recommending resources and considerations about communicating professional opinions to owners regarding online information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".