Bibliographic record
Abstract
Tens of thousands of abandoned and orphaned animals are taken in by rescue organizations across Canada every year. These animals are then rehabilitated by caring individuals who often work as volunteers. While the plight of the animals and the financial toll associated with their rescue is made clear through aid requests by organizations, very little attention has been paid to the effect this work has on the humans involved (Englefield, Starling, & McGreevy, 2018). Research has revealed that animal health care professionals (AHCPs), such as veterinarians, experience higher-than-average levels of psychological distress due to the nature of their work (Nett et al., 2015; Polachek & Wallace, 2018). Animal rescue workers (ARWs) experience many of the same stressors as AHCPs, and what little research has examined ARWs suggests that the psychological consequences they face may be even more severe (Figley & Roop, 2006). Our study was designed to systematically examine the work-related stressors and associated mental health ramifications of animal rescue workers across Canada. As predicted, we found significant correlations between respondent scores on measures of depression, compassion fatigue (which includes secondary traumatic stress and burnout), and trauma. Furthermore, scores on some measures were correlated with the types of animals rescued, and the tasks that ARWs performed. Overall then, our research demonstrates that depending on the stressors that they are exposed to, individuals who work in animal rescue within Canada are at risk of experiencing detrimental psychological outcomes associated with their work. Presented in absentia on April 27, 2020 at Student Research Day at MacEwan University in Edmonton, Alberta. (Conference cancelled) Faculty Mentor: Eric Legge Department: Psychology NOTE: This work is available to MacEwan users only at https://roam.macewan.ca/islandora/object/gm:2137
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".