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Record W2172072048 · doi:10.1177/0886260513520506

Survivor’s Costs of Saying No

2014· article· en· W2172072048 on OpenAlexaff
Carla Pajak, Farah Ahmad, Angélique Jenney, Penny Fisher, Linda Chan

Bibliographic record

VenueJournal of Interpersonal Violence · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsYork UniversityUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsDomestic violenceContext (archaeology)Mental healthPsychologyPoison controlOccupational safety and healthSuicide preventionAbusive relationshipNursingHuman factors and ergonomicsMedicinePsychiatryMedical emergency

Abstract

fetched live from OpenAlex

Intimate partner violence (IPV) is a crucial public health concern with substantial detrimental effects, including poorer physical and mental health as well as increased difficulties accessing formal services. Most research to date has focused on frequencies, barriers, and facilitators of service use among IPV survivors. However, what remains poorly understood is the perspectives of IPV survivors on their experiences of accessing multiple services after leaving the abusive situations. To answer this, six one-on-one semi-structured interviews were conducted with survivors using expanded definition of "services," which included social services, shelters, health care, police, legal assistance, and so forth. Data were analyzed using Constant Comparison. Four resulting themes were (a) Positive Aspects, (b) Negative Aspects, (c) Impact of Experiences With Services, and (d) Contextual Factors. Within each of these categories, several sub-categories emerged and are discussed within the context of the literature and recommendations are made for improving services for IPV survivors.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0010.000
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 head0.016
GPT teacher head0.311
Teacher spread0.295 · 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 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

Citations17
Published2014
Admission routes1
Has abstractyes

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