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Animal‐Assisted Therapy for Older Adults

2015· other· en· W2346947357 on OpenAlexaff
Javier Virúes‐Ortega, Alison D. Cox

Bibliographic record

VenueThe Encyclopedia of Adulthood and Aging · 2015
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsAnimal-assisted therapyAnxietyDementiaDepression (economics)Exposure therapyAnimal studiesPsychologyClinical psychologyPopulationPsychological interventionMedicineAnimal welfarePsychiatryPet therapyDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Animal‐assisted therapy comprises planned human–animal interaction episodes intended to induce a variety of behavioral and physiological responses in various clinical populations and in normal populations with special needs. Companion animals can cause stress‐buffering effects and the enhancement of social functioning in the normal adult population. Also, pets seem to induce gains in social functioning among both cognitively intact and cognitively impaired older adults. Individuals with dementia exposed to animal‐assisted therapy have shown reduced levels of behavioral disturbances. The effect sizes of these outcomes are compatible with clinically significant effects. No clear effects of animal‐assisted therapy have been reported for activities of daily living and cognitive functioning, while effects on anxiety and depression may not be clinically significant. There is preliminary evidence to suggest that guided, short‐term interventions delivered individually could help to maximize the effects of animal‐assisted therapy for older adults.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.003

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.

Opus teacher head0.013
GPT teacher head0.311
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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