Shelter-Specific Occupational Stress among Employees in Animal Shelters
Bibliographic record
Abstract
Abstract In the past 10 years a small number of articles have examined the personal and organizational costs associated with euthanasia-related strain among employees in animal shelters. However, there is very little research focusing on a wider range of potentialstressors especially without a pre-existing assumption that euthanasia is the most stressful part of the job. A few studies have identified other sources of stress among shelter workers. Therefore, the purpose of the present study is to investigate the range of factors that may contribute to occupational stress in shelter workers. This study utilized a semi-structured interview. The participants were 22 shelter employees from seven shelters in Ontario, Canada and the northeastern United States. Data were analyzed using the constant comparative method. Three categories of stressors were identified: a) those that are unique or largely unique to the shelter setting; b) those that can be found in a wide variety of occupations; and c) those that can be found in a wide variety of occupations, but are played out in unique ways in shelters. The stressors in the first category are reported here. Although euthanasia was a significant factor for 21 participants, five other major sources of stress were identified. Twenty-one participants cited the public’s perceptions of animal shelters and 18 cited rude and abusive human clients. Twelve cited relationships with the animals including attachment issues. Eight identified responsibility for life as a stressor. Eleven identified witnessing animal suffering.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".