Recognizing Animals as an Important Part of Helping
Bibliographic record
Abstract
The beneficial role of companion animals on human health and wellbeing across the life span is well documented in the rapidly expanding multi disciplinary body of literature known as human animal interactions (HAI). Social workers practice at the interface of people and their diverse environments. The presence of human animal bonds (HAB) within client systems, between people and companion animals in particular, are increasingly acknowledged and valued by social workers. Additionally, some social workers incorporate animals in their practice through animal assisted interventions (AAI). However, there is a paucity of empirical literature on social workers’ knowledge about and experiences with the inclusion of animals. We conducted a survey across three prairie provinces in Canada, replicating a study that was first implemented nationwide in the U.S. and later in the Canadian province of Nova Scotia. The survey explored social workers’ knowledge of HAI in social work. The results, similar to the Nova Scotia and U.S. findings, suggest that s social workers have general knowledge about HAI and the HAB, and that some do incorporate animals in practice. Social workers seem to have increasing knowledge and skills about HAI. While this is a positive trend, there is nonetheless a need for specialized education and training on the beneficial impact that companion animals can have on social work practice. In this paper, the application of zooeyia within social work is adopted as one approach to understanding HAB. Important implications for human health and wellbeing and social work practice at the practitioner and organizational levels are discussed.
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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 | 0.010 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".