Comments on the dilemma in the April issue: Enrolling in animal‐assisted therapy programmes
Bibliographic record
Abstract
In the dilemma discussed in the April issue of In Practice , Simon Coghlan described a scenario where a long‐standing client wishes to enrol her dog in an animal‐assisted therapy (AAT) programme for children with autism spectrum disorder. The AAT psychologist has requested a veterinary assessment before the dog (Imogen) takes part in the programme. Imogen is a very calm, well‐trained 18‐month‐old Labrador. She has recently been diagnosed with unilateral elbow dysplasia. Her owner reports that Imogen dislikes having her nails trimmed. Given this, you wonder if you have an ethical responsibility to recommend against the dog entering AAT. However, your client is very upbeat, ‘Imogen loves children and will be a wonderful help to them,’ she enthuses. ( IP , April 2019, vol 41, pp 134‐135). What do you do?
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.
How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".