Physical health caregiver, mental wellness supporter, and overall well-being advocate: Women's roles towards animal welfare during the COVID-19 emergency response
Bibliographic record
Abstract
Women's health-specific contributions in emergency response stages pertain primarily to family and community-based rescue and support-focused roles. As disasters affect both human beings and their animal co-inhabitants, comprehensive literature exploring women's contributions towards companion animal welfare in emergency response settings remains sparse. COVID-19-triggered public health mitigation strategies caused diverse challenges relating to veterinary medical service access, thus establishing a platform for a nuanced exploration of gendered roles vis-a-vis animal health and well-being during the initial COVID-19 emergency response period. This project employs a semi-structured interview approach to qualitatively investigate the roles, responsibilities, and experiences of twelve people, eleven of whom self-identify as women, who cared for animal co-inhabitants while seeking veterinary medical services during the COVID-19 emergency response in Metro Vancouver, British Columbia, Canada. This research identifies three primary animal welfare-related roles that woman companion animal guardians (WCAGs) assumed during the COVID-19 emergency response period: 1) Companion animal physical health caregiver, spanning from nuclear to extended families and into the community; 2) Companion animal mental wellness supporter, associated with human-animal interactions in family/household, community, and veterinary clinic settings; 3) Companion animal holistic well-being advocate, utilizing various strategies at family, community, and societal levels. Understanding gender-specific animal welfare contributions in an emergency response setting narrows knowledge gaps and provides WCAGs and animal welfare-related public, private, and not-for-profit sectors with evidence-based strategies for emergency response planning improvements, supporting healthy and sustainable human-animal bonds in the current COVID-19 pandemic and future extreme events.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".