The impact of the COVID-19 pandemic on a cohort of Labrador Retrievers in England
Bibliographic record
Abstract
Abstract BackgroundThe COVID-19 pandemic is likely to have affected the welfare and health of dogs, due to surges in adoptions and purchases, changes in the physical and mental health and financial status of dog owners, changes in dogs’ lifestyle and routines and limited access to veterinary care. Research is needed to be able to assess the extent of these effects. The aims of this study were to investigate whether COVID-19 restrictions were associated with differences in Labrador Retrievers’ lifestyle, routine care, insurance status, illness incidence or veterinary attendance with an illness, who were living in England and enrolled in Dogslife, an owner-based cohort study. Longitudinal questionnaire data from Dogslife that was relevant to the dates between the 23rd of March and the 4th of July 2020, during COVID-19 restrictions in England, were compared to data between the same dates in previous years from 2011 to 2019 using mixed regression models and adjusted chi-squared tests. ResultsThe COVID-19 restrictions study period (March 23rd to July 4th 2020) was associated with owners who enrolled in Dogslife reporting increases in dogs’ exercise and worming and decreases in insurance, titbit-feeding and vaccination, in comparison with previous years (March 23rd to July 4th, 2010 to 2019). There were decreased odds of owners reporting that their dogs had an episode of coughing (0.20, 95% CI: 0.04 – 0.92) and that they took their dog to a veterinarian with an episode of any illness (0.58, 95% CI: 0.45 – 0.76) during the COVID-19 restrictions and owners were even less likely to take their dog to a veterinarian with certain illnesses. ConclusionsDogslife provided a unique opportunity to study prospective questionnaire data from owners already enrolled on a longitudinal cohort study, which minimised bias associated with recalling events prior to the pandemic, allowed a wider population of dogs to be studied than is available from primary care data and offered unique insights into owners’ decision making about their dogs’ healthcare. There are clear implications of the COVID-19 pandemic and associated restrictions for the lifestyle, care and health of dogs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".