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Record W2603094390 · doi:10.1177/0886260517700618

Examining the Relationship Between Intimate Partner Violence and Concern for Animal Care and Safekeeping

2017· article· en· W2603094390 on OpenAlexaffabout
Melissa A. Wuerch, Crystal J. Giesbrecht, Jill A. B. Price, Tracy Knutson, F.-Sophie Wach

Bibliographic record

VenueJournal of Interpersonal Violence · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsSaskatchewan HealthUniversity of Regina
Fundersnot available
KeywordsThematic analysisDomestic violenceExploratory researchQualitative researchContext (archaeology)NursingPsychologyPoison controlSuicide preventionHuman factors and ergonomicsService providerMedicineService (business)Medical emergencySociologyBusiness

Abstract

fetched live from OpenAlex

The current study examined the knowledge and experience of animal welfare and human service providers in urban and rural communities of Saskatchewan, Canada. Nine exploratory qualitative interviews were conducted to gather a more in-depth understanding of whether the concern for animal care and safekeeping impacts the decision to leave situations of intimate partner violence. The interviews were semistructured and guided by four questions, which were designed, reviewed, and revised based on feedback from a community-based research team. Thematic analysis highlighted important findings, allowing for the generation of suggestions for improvement of current supports and services offered. The current study findings suggest that concern for animal care and safekeeping creates significant barriers regarding the decision to leave situations of intimate partner violence and abuse, warranting further research to inform support services and resources within a Canadian context.

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)0.014
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 score0.831
Threshold uncertainty score0.340

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0080.006
Scholarly communication0.0030.001
Open science0.0010.005
Research integrity0.0010.002
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 head0.093
GPT teacher head0.398
Teacher spread0.305 · 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

Citations19
Published2017
Admission routes2
Has abstractyes

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