Professional characteristics, attitudes, and practices associated with stress and quality of life among Canadian animal health workers.
Bibliographic record
Abstract
Objective: To describe the knowledge, attitudes, and practices (KAP) towards COVID-19 of Canadian companion animal health workers (AHW); to measure their perceived stress and quality of life (QoL); and to explore professional risk factors associated with stress and QoL. Sample: We sampled 436 companion animal veterinarians and technicians. Procedure: The study had cross-sectional and cohort components. It was conducted online in August to December 2020, and repeated in May to July 2021, using a questionnaire assessing the respondents' professional characteristics, COVID-19 KAP, perceived stress, and QoL. Results: Overall, AHW had sufficient knowledge of COVID-19 transmission, and reported having adopted good preventive practices. Since the beginning of the pandemic, participants reported increases in new clients (76%), in refusal of new clients (53%), and in pet euthanasia (24%). Increased client refusal and pet euthanasia were associated with greater stress and poorer professional QoL, whereas perceived susceptibility to and adoption of measures against COVID-19 were associated with lower stress and better QoL. Conclusion and clinical relevance: For AHW, professional characteristics were associated with stress and professional QoL. This information is important for developing strategies to cope with the ongoing shortage of AHW and with future public health crises.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".