Re-imagining Animal-Assisted Human Services (AAHS): Developing Canada’s first voluntary National Standard of Canada (NSC) for AAHS
Bibliographic record
Abstract
Abstract Animal-Assisted Human Services (AAHS) is a variety of interdisciplinary practices, including assistance/service animals and Animal-Assisted Interventions such as Animal-Assisted Activities, Animal-Assisted Learning, and Animal-Assisted Therapy. The Human-Animal Bond and Human-Animal Interactions (HAI) bridge the Natural Sciences, Humanities, Veterinary Medicine, and Applied Sciences. Over the last century, AAHS has gradually evolved into a booming, lucrative marketplace characterized by for-profit and non-profit businesses within Canada’s economy. Even so, there are no integrated national, provincial, or territorial frameworks for AAHS within Canada’s healthcare, social, justice, and correctional services on which its human services and economy are built. By the same token, the lines remain blurred concerning the essential competencies and credentials required to work or volunteer within the industry or how or where to begin pursuing a career within this rapidly growing ecosystem. Consequently, AAHS is still in its infancy as a recognized sub-category within Canada’s Human Services Industry. Additionally, this broad, multifaceted industry encompasses silos, such as the horse and dog industries. While both industries provide AAHS, their self-contained environments inhibit opportunities to cross-pollinate their knowledge, experiential learning, and expertise in theory and practice. Therefore, uniting and converging related industries within this milieu would help to open doors to new possibilities, innovations, and relationships that would not be possible otherwise. The correlation with HAI makes these human services a distinct discipline in its own right. For over two decades, The Canadian Foundation for Animal-Assisted Support Services (CF4AASS), an impartial national registered charity, has promoted the availability, credibility, and sustainability of excellence in AAHS. Co-designing this industry sector standard was a catalyst for integrating and engaging relevant stakeholders to cultivate mutually beneficial outcomes and building blocks toward a seamless national AAHS Centre of Excellence. Subsequently, re-imagining AAHS was a call to action where opportunity and shared responsibilities intersect. Embracing an integrated approach to foster unity in diversity and the co-innovation of AAHS, its marketplace, and the environments in which it interconnects is rooted in CF4AASS’s culture and support services. Illustrating the industry sector’s combined value, national footprint, and socio-ecological and socio-economic Systems (SES) impact would significantly enhance this promising sector’s complementary and essential human services contributions throughout Canada. With this in mind, I hope this commentary sheds light on why the development of a voluntary standard was long overdue and a proactive measure to benefit multiple stakeholders, a step forward to nurture and facilitate solidarity and innovation.
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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 | 0.041 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".