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Record W4405054147 · doi:10.1101/2024.12.02.24318365

Navigating online information access for women survivors of intimate partner violence living in long term shelters

2024· preprint· en· W4405054147 on OpenAlexaffabout
Ebony Rempel, Lorie Donelle, Jodi Hall

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsFanshawe CollegeWestern University
Fundersnot available
KeywordsThematic analysisService providerPublic relationsInternet privacyGovernment (linguistics)BusinessStalkingSocial mediaService (business)PsychologyQualitative researchPolitical scienceSociologyWorld Wide WebMarketingComputer science

Abstract

fetched live from OpenAlex

Abstract This study explores the use of online resources by women who experienced intimate partner violence (IPV) and were living in second-stage shelters. Given the ubiquity of online access across all aspects of everyday life—from health care and education to job searching and social support—ensuring equitable digital access is essential for everyone. This study used purposive sampling and thematic analysis of in-depth, in-person interviews with women residing in second-stage shelters across Alberta, Canada, to explore their experiences with online resources for support and information. Thematic analysis identified three main themes: Proactive Preparation, Staying Connected to Support Networks, and Barriers to Online Access - highlighting the critical role of digital resources in empowering participants but also underscoring significant challenges, such as financial constraints, internet reliability, and privacy concerns. Participants emphasized the importance of online resources for maintaining relationships, preparing for meetings with service providers, and accessing information and support. However, they faced significant challenges, including financial constraints, lack of reliable internet access, and privacy concerns. The findings underscore the need for improved digital access, health equity, and tailored digital literacy programs to support IPV survivors effectively. While social media and online platforms provide vital support and information, they also pose risks of digital surveillance and stalking. The study advocates for a collaborative effort from government agencies, service providers, healthcare providers, technology companies, and community organizations to create comprehensive support systems. Addressing these barriers can enhance the accessibility of crucial information and resources, empowering women on their journey towards recovery and independence. Introduction Author Summary The researchers shed light on the experiences of women who have experienced IPV who seek information and support through online resources while residing in second-stage shelters. Recognizing that digital access has become a staple of modern life, our research investigates how these women navigate online spaces to support their journey towards recovery. Through interviews with women across Alberta, Canada, we identified critical themes: the need for proactive information gathering, maintaining connections with support networks, and the challenges posed by limited online access. Participants spoke to the value of digital resources for maintaining relationships and preparing for important interactions with service providers, while also facing significant barriers like financial constraints, unreliable internet, and privacy risks. Our findings call for collaborative efforts from service providers, policymakers, and technology companies to improve digital accessibility, privacy safeguards, and tailored literacy programs. By addressing these obstacles, we aim to empower women in second-stage shelters, helping them build self-efficacy and resilience through secure and supportive online environments.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.034
GPT teacher head0.383
Teacher spread0.349 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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