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Record W4235853473 · doi:10.24124/2015/bpgub1661

Animal assisted crisis response and creating connections: a practical guide for implementing therapy dogs into a victim services policing environment

2015· dissertation· en· W4235853473 on OpenAlexaffabout
Krista L. E. Levar

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsExperiential learningPublic relationsEngineeringPsychologyPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

This project was created to address the needs of police based victim services units as they implement a therapy dog program into police detachments. Taking the form of a manual, this project examines the many considerations of implementing a program that has very little precedent in Canada and no precedent at all within the Royal Canadian Mounted Police (RCMP). The manual is broken down into distinct sections with headings and sub-headings that address different aspects of each component listed. The main content is factual and experiential in nature and will lead the reader through the considerations of implementation as well as offering examples of the program in practice. The project looks at the historical precedence of animal assisted therapy and explores its challenges, findings and successes while also discovering the unexpected benefit of the therapy dogs' presence within the detachment itself and how this soft approach to grounding helps clients, officers and victim services workers alike. --Leaf ii.

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)0.006
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.005
Scholarly communication0.0060.006
Open science0.0030.006
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0340.022

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 head0.037
GPT teacher head0.431
Teacher spread0.394 · 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 designQualitative
Domainnot available
GenreEmpirical

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 routes2
Has abstractyes

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