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Record W3029894255 · doi:10.1177/0886260520920867

Sharing Personal Experiences of Accessibility and Knowledge of Violence: A Qualitative Study

2020· article· en· W3029894255 on OpenAlexaffabout
Tara Mantler, Kimberley T. Jackson, Edmund J. Walsh, Selma Tobah, Katie J. Shillington, Brianna Jackson, Emily da Silva Soares

Bibliographic record

VenueJournal of Interpersonal Violence · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsWestern University
Fundersnot available
KeywordsDomestic violenceRuralityQualitative researchContext (archaeology)Health carePoison controlSuicide preventionMedicineOccupational safety and healthNursingRural areaPsychologyEnvironmental healthSociologyPolitical scienceGeography

Abstract

fetched live from OpenAlex

In North America, the most common societal response to intimate partner violence (IPV) has been the establishment of women's shelters for temporary housing and security. Rurality further compounds the challenges women experiencing IPV face, with unique barriers from their urban counterparts. This study sought to explore the intersection of rural women's health care experiences within the context of IPV. Eight rural women living in Southwestern Ontario, who had experienced IPV, had used women's shelter services, and who had accessed health care services in the preceding 6 months were interviewed. Using a feminist, intersectional lens, we collected and analyzed qualitative data using an interpretive description approach. Findings demonstrated that women were able to identify strengths and opportunities from their experiences, but significant challenges also exist for rural women seeking health care who experience IPV. Our findings underscore the need for filling of policy gaps between health care and the services women use. We propose that further research is needed on alternative, integrated models of shelter services that address health care needs for women experiencing IPV.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.065
GPT teacher head0.424
Teacher spread0.359 · 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 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

Citations5
Published2020
Admission routes2
Has abstractyes

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