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Record W4415586627 · doi:10.21083/crrf.v29i1.7749

Coping with and Responding to Challenges:A Comparative Case StudyBetween Service Providers Supporting Those Experiencing Intimate Partner Violence

2025· article· W4415586627 on OpenAlexaff
Meghan Wrathall, Rachel Herron

Bibliographic record

VenueProceedings of the Canadian Rural Revitalization Foundation · 2025
Typearticle
Language
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsBrandon University
Fundersnot available
KeywordsService providerDomestic violenceComparative caseCoping (psychology)SustainabilityService (business)Poison controlService delivery framework

Abstract

fetched live from OpenAlex

This project examines the challenges faced by service providers supporting those experiencing Intimate Partner Violence (IPV) in their regional service centre and surrounding rural communities. Two case study sites, Brandon, MB and Sydney, NS, were selected to examine the experiences and perceptions of service providers using semi-structured interviews (N=15). The service providers in both studies identified two main themes, underfunding and understaffing, which create challenges to adequate service provision, especially in reaching rural areas. Strategic use of collaboration was identified as a means to deal with such challenges. The service providers used collaboration to create connections between the agencies and those who use them, stay visible in the community, provide more comprehensive services, and assess individual and community needs. Additionally, collaboration was identified as the future community-level strategy for providing intervention and preventive education and awareness, with the aim of changing community perceptions of IPV. This project identifies opportunities for future collaborative efforts while also raising questions about the sustainability of these collaborations, given the lack of resources available to sustain them.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0030.000
Scholarly communication0.0010.002
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 head0.051
GPT teacher head0.362
Teacher spread0.310 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Canadian Rural Revitalization FoundationSame topicIntimate Partner and Family ViolenceFrench-language works237,207