Animal-Assisted Interventions in Paediatric Oncology: The Story of Francesco and His Friend Megan
Bibliographic record
Abstract
Inviting InnovationsAnimal-Assisted Interventions (AAI) allow for the creation of meaningful relationships between people and animals: AAI's aims are therapeutic, educational, and social.Designed to increase a person's sense of wellbeing, this type of intervention is increasingly used in paediatric oncology [7,8].The literature describes how the presence of animals in a hospital setting can be a distraction, source of pleasure, and therapy for children [9] improving their mood and counteracting the boredom, fear, pain and anxiety connected to hospitalisation [10].Although there is some literature regarding the efficacy of AAI.This article presents the story of Francesco, a boy of 9 with leukaemia, and his sessions with Megan, a 10-year-old Labrador AbstractChildren affected by neoplasia face extended periods of hospitalisation and lengthy, invasive courses of treatment.Complementary non-pharmacological therapies, as Animal-Assisted Interventions (AAI), are more frequently being used and integrated alongside traditional forms of treatment with the objective of easing adaptation to the hospital environment.AAI is an umbrella term that includes animal-assisted activities (AAA), animal-assisted therapy (AAT), and animal-assisted education (AAE) and AAI Resident animals (RA) Animal-Assisted Interventions (AAI) allow for the creation of meaningful relationships between people and animals: AAI's aims are therapeutic, educational, and social, and are designed to increase a person's sense of wellbeing.This case-report presents the story of Francesco, a boy of 9 with leukaemia, and his sessions with Megan, a 10-year-old Labrador Retriever.The ways in which AAI has allowed Francesco to counter boredom, fear, pain, and anxiety related to hospitalization are illustrated.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| 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".