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Record W4304782695 · doi:10.1079/hai.2022.0009

“My Lifeline is Gone”: An Exploration of the Experiences of Veterans Following the Loss of their Psychiatric Service Dog(s)

2022· article· en· W4304782695 on OpenAlexaffabout
Maryellen Gibson, Darlene Chalmers, Siyu Ru

Bibliographic record

VenueHuman-animal interaction bulletin · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of ReginaUniversity of Saskatchewan
Fundersnot available
KeywordsGriefCoping (psychology)Exploratory researchPsychologyService memberPsychiatryLife spanQualitative researchTraumatic griefService (business)MedicineNursingGerontologyMilitary personnel

Abstract

fetched live from OpenAlex

Abstract Canadian veterans with PTSD are increasingly accessing psychiatric service dogs as a complementary treatment for their symptoms. Due to the short life span of dogs, however, it is inevitable that these veterans will experience the loss of their PSD either through death, retirement, or relinquishment. This exploratory qualitative study shares the findings from interviews with four veterans who had experienced grief at the loss of a PSD. The themes that emerged suggest that participants experienced a grief cycle: building of a bond with their PSD, a grief response after the loss of their PSD, healing and coping, and a transitional stage when introducing a new PSD into their lives. Recommendations are made for health care professionals working with veterans with psychiatric service dogs and for service dog providers and organizations to better prepare and support their clients leading up to and after the loss of their service dog.

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)0.004
metaresearch head score (Gemma)0.009
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0140.009
Scholarly communication0.0050.003
Open science0.0020.006
Research integrity0.0020.005
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 head0.048
GPT teacher head0.362
Teacher spread0.314 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

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