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Record W2060838329 · doi:10.1108/jfp-09-2014-0031

The therapeutic potential of a prison-based animal programme in the UK

2015· article· en· W2060838329 on OpenAlexaboutno aff
Jenny Mercer, Kerry Gibson, Debbie Clayton

Bibliographic record

VenueJournal of Forensic Practice · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPrisonThematic analysisOriginalityExploratory researchPsychologyUnit (ring theory)Applied psychologyCriminologySocial psychologyQualitative researchSociologySocial science

Abstract

fetched live from OpenAlex

Purpose – Much evidence suggests that animals can serve as therapeutic tools for those working with vulnerable individuals. This exploratory study analysed the accounts of staff and offenders involved in a UK prison-based animal programme. The purpose of this paper was to explore the perceived impact of such a programme with male offenders. Design/methodology/approach – Semi-structured interviews were conducted with three service users and five staff members. Participants were drawn from a special unit in a category B prison which housed an animal centre. Findings – A thematic analysis identified four salient themes: a sense of responsibility, building trust, enhanced communication, and impact on mood and behaviour. Findings revealed that offenders seemed to gain particular benefit from interacting with the two Labrador dogs which were present on the wing. Practical implications – The study highlights the therapeutic potential of the presence of animals in prisons. Their implications of this for forensic practice are discussed. Originality/value – This paper offers an important contribution to the sparse literature about prison-based animal programmes in the UK.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.006
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.040
GPT teacher head0.366
Teacher spread0.327 · 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 designObservational
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

Citations36
Published2015
Admission routes1
Has abstractyes

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