A Study On Pet Health Insurance And Its Impact On Owners Spending For Veterinary Services With Reference To Mysuru City
Bibliographic record
Abstract
Pet health insurance business has the potential to grow in India because of the higher number of pet adoption and the trend of pet-loving lifestyle, but it is unorganised sector in India because lack of awareness. The pet population is increasing in India, but access to veterinary care continues to be a concern. Affordability to pay for veterinary services is a challenge for some pet owners. The presence of pet health insurance is one tool that can help alleviate this burden. This paper estimated awareness and willingness-to-pay for pet insurance, veterinary visit and expenditure and driving forces behind dog owner choice regarding health care for their dog. The quantitative method with online questionnaire was conducted by focusing on the dog owners and 235 responses were collected in Mysuru region. Several tests were applied such as descriptive statistics, reliability test, factor analysis, correlation and regression, it was found that veterinary visit, expenditure, pet responsibility, and prevention of financial risk from pet adoption had a significant and positive impact on purchasing of pet health insurance. The result shows that 74% of dog owners were not aware of pet insurance, 75% agrees that veterinary cares are costly and causes financial stress and 88% of dog owners are willing to pay for pet health insurance. Findings from this study can help the pet health insurance company to build an effective marketing strategy that leads to effective growth in India. Further research should explore about reason to purchase pet insurance, what are the barriers of purchasing pet insurance and how it affects the pet owner behavior.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".