Readability and content of online pet obesity information
Bibliographic record
Abstract
OBJECTIVE: To assess the readability of pet obesity information, document the presence and absence of types of pet obesity information, and perform comparisons between dog and cat obesity information content on websites. SAMPLE: 68 websites containing pet obesity content. PROCEDURES: Websites were systematically retrieved with a search engine and predefined search terms and phrases. For each website, pet obesity information was scored by use of 2 established readability tools: the simple measure of gobbledygook (SMOG) index and Flesch-Kincaid (FK) readability test. A directed content analysis was conducted with a codebook that assessed the presence or absence of 103 variables across 5 main topics related to pet obesity on each website. RESULTS: The mean reading grade levels determined with the SMOG index and FK readability test were 16.61 and 9.07, respectively. Instructions for weight measurement and body condition scoring were found infrequently, as were nonmodifiable risk factors. There was a greater focus on addressing obesity through dietary changes than through increasing physical activity. Few websites recommended regular follow-up appointments with veterinarians. Weight management information and the emphasis on owners' commitment to achieve their pet's weight loss targets differed among dog- and cat-focused websites. CONCLUSIONS AND CLINICAL RELEVANCE: Results indicated that pet obesity information on the studied websites was largely inaccessible to pet owners owing to the associated high reading grade levels. Readers of that information would benefit from clarification of information gaps along with provision of guidance regarding navigating online information and counseling on the importance of nutritional and dietary reassessments for individual pets performed by veterinarians.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.004 | 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".