Interviews with Indian Animal Shelter Staff: Similarities and Differences in Challenges and Resiliency Factors Compared to Western Counterparts
Bibliographic record
Abstract
Animal shelters in India are at the forefront of efforts to improve free-ranging dog welfare and tackle animal overpopulation. In terms of cultural and political context, access to resources, and public health challenges, they operate in a very different environment than Western counterparts. Despite these distinctions, current sheltering literature is largely centered around countries such as the United States. The goal of this exploratory study was to examine the experiences of Indian animal shelter staff. Researchers conducted ten semi-structured interviews, in a mix of Hindi and English, with managers, veterinary nurses, and animal caretakers from three shelters. Using thematic analysis, shelter challenges as well as resiliency factors that enable staff to cope with these challenges were identified. Key challenges were inadequate funding, community conflict, and high intake numbers. Resiliency factors included flexibility, duty of care, co-worker relationships, and understanding animal needs. The results of this qualitative study revealed that the experiences of shelter staff are shaped by social, political, and cultural factors and that there is a need for further, context specific research on Indian sheltering rather than only relying on Western perspectives.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".