A magical dream: A pilot project in animal-assisted therapy in pediatric oncology
Bibliographic record
Abstract
For children with cancer, being hospitalized represents a great source of stress. Hospitalized children are not only deprived of their familiar and comforting world, but they must also face various and often painful treatments. They must quickly adapt to new people and to an environment that is very different from that of their homes. They have greater safety needs. Thus, it is important to offer these children concrete ways to better adapt to the stresses of hospitalization. Animal-assisted therapy, considered within this project as a novel approach to care, constitutes an interesting solution. It involves using the privileged relation between children and animals to foster the process of adaptation to illness and the hospital environment. The experience described in this article is a one-year pilot project completed on a pediatric oncology unit. A priori, an already very heavy workload, the vulnerability of the patients, and many constraints added to the concerns related to the presence of animals on a tertiary care unit. A postiori, the rigorous design and implementation process of the pilot project, the strong involvement and engagement of volunteers and professionals, the quality of the participating "therapeutic" dogs, the originality of the entire process, and the satisfaction of the patients and nursing staff contributed to its success and to establishing the basis for a permanent implementation of this special care program for children hospitalized with cancer.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".