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Record W2104027515 · doi:10.5402/2012/239468

Identifying the Turning Point: Using the Transtheoretical Model of Change to Map Intimate Partner Violence Disclosure in Emergency Department Settings

2012· article· en· W2104027515 on OpenAlexafffund
Cristina Catallo, Susan M. Jack, Donna Ciliska, Harriet L. MacMillan

Bibliographic record

VenueISRN Nursing · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsMcMaster UniversityToronto Metropolitan University
FundersOntario Ministry of Health and Long-Term Care
KeywordsTranstheoretical modelEmergency departmentDomestic violencePsychologyNursingMedicineMedical emergencyApplied psychologySuicide preventionPoison controlPsychological intervention

Abstract

fetched live from OpenAlex

Background. The transtheoretical model of change (TTM) was used as a framework to examine the steps that women took to disclose intimate partner violence (IPV) in urban emergency departments. Methods. Mapping methods portrayed the evolving nature of decisions that facilitated or inhibited disclosure. This paper is a secondary analysis of qualitative data from a mixed methods study that explored abused women's decision making process about IPV disclosure. Findings. Change maps were created for 19 participants with movement from the precontemplation to the maintenance stages of the model. Disclosure often occurred after a significant "turning point event" combined with a series of smaller events over a period of time. The significant life event often involved a weighing of options where participants considered the perceived risks against the potential benefits of disclosure. Conclusions. Abused women experienced intrusion from the chaotic nature of the emergency department. IPV disclosure was perceived as a positive experience when participants trusted the health care provider and felt control over their decisions to disclose IPV. Practice Implications. Nurses can use these findings to gauge the readiness of women to disclose IPV in the emergency department setting.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.087
GPT teacher head0.386
Teacher spread0.299 · 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

Citations29
Published2012
Admission routes2
Has abstractyes

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