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Record W2888369835 · doi:10.1177/0886260518795503

Intimate Partner Violence and Concern for Animal Care and Safekeeping: Experiences of Service Providers in Canada

2018· article· en· W2888369835 on OpenAlexaffabout
Melissa A. Wuerch, Crystal J. Giesbrecht, Nicole Jeffrey, Tracy Knutson, F.-Sophie Wach

Bibliographic record

VenueJournal of Interpersonal Violence · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of GuelphUniversity of Regina
Fundersnot available
KeywordsDomestic violenceService providerAnimal welfareNursingPoison controlWelfareSuicide preventionMedicineAbusive relationshipService (business)Human factors and ergonomicsOccupational safety and healthQualitative researchPsychologyEnvironmental healthBusinessPolitical scienceSociologyMarketing

Abstract

fetched live from OpenAlex

The present study examined the experiences of animal welfare and intimate partner violence service providers living in urban, rural, and northern communities in Saskatchewan, Canada. Two online surveys were distributed among animal welfare and intimate partner violence service providers across the province. Quantitative and qualitative information was obtained to further understand how concern for animal care and safekeeping impacts the decision to leave an abusive relationship. The questions asked in the online surveys were designed, reviewed, and revised based on feedback from a community-based project advisory team. Descriptive statistics and detailed comments highlighted important findings and suggestions for improvement. Findings suggest that concern for animal care and safekeeping creates challenges for individuals leaving abusive partners, especially those living in rural and northern communities, and further demonstrate the importance of collaboration between animal welfare and intimate partner violence service providers. Further research is warranted to inform and improve the development and implementation of national support services and resources.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.327
Teacher spread0.309 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations12
Published2018
Admission routes2
Has abstractyes

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