Recognizing Animals as an Important Part of Helping
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it