A Qualitative Analysis of Management Perspectives on Seeking to Implement the Foster Cat Project in Residential Aged Care in the Context of COVID-19
Bibliographic record
Abstract
This study explores the challenges facing a pilot project aiming to foster homeless cats in an Australian residential aged care facility. The global COVID-19 pandemic stalled the project but also presented an opportunity to gain reflective insights into the perceived barriers, enablers and tensions involved in seeking to implement pet animal inclusion in residential aged care. Perspectives from aged care management, animal welfare services and researchers/project managers were all sought using semi-structured interviews, and themes developed using a qualitative descriptive analysis. Perceived barriers to the project before and after the pandemic were not dissimilar with four key themes emerging: competing priorities, risk and safety, resources, and timing. All existed differently across stakeholder groups creating tensions to be negotiated. These themes are then mapped to the competencies established by the International Union of Health Promotion and Education (IUHPE) for undertaking health promotion, demonstrating that this skill base can be drawn on when seeking to implement human-animal inclusive projects. Creating supportive healthful environments for frail older persons is a moral imperative of extended lives. Health Promotion skills as outlined in the Ottawa Charter and IUHPE competencies for health promotion workers need to be extended to include animal services, agendas and cultures to promote multi-species health promotion into the future.
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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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".