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Record W7117486887 · doi:10.1111/fare.70080

Understanding revenge cognitions among Jewish women survivors of intimate partner violence in Canada

2025· article· en· W7117486887 on OpenAlexaboutno aff
Anat Vass

Bibliographic record

VenueFamily Relations · 2025
Typearticle
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsnot available
FundersEuropean Commission
KeywordsCoping (psychology)Domestic violenceJudaismCognitionEmpowermentConstructiveHuman factors and ergonomics

Abstract

fetched live from OpenAlex

Abstract Background Revenge cognitions and behaviors are common responses following intimate partner violence (IPV) victimization, yet little is known about how survivors, particularly from religious minority communities, process these responses during recovery. Objective This study investigated how Jewish women IPV survivors conceptualize and navigate revenge‐related responses in the aftermath of IPV. Method Using a descriptive phenomenological‐psychological approach, data were collected through in‐depth interviews and focus groups with 79 Jewish Canadian women (aged 24–64) who had experienced IPV. Results Thematic analysis revealed three patterns: (a) First, “revenge—between thinking, planning, and acting,” capturing retaliatory cognitions; (b) second, “silence—the ultimate revenge,” demonstrating nonengagement as empowerment as a psychological coping strategy; and (c) third, “true winning has nothing to do with revenge,” highlighting transformation toward self‐focused recovery. Although revenge thoughts were acknowledged as inherent to early healing stages, findings showed these typically evolved toward constructive healing paths when supported by culturally informed approaches. Conclusion Findings demonstrate that although revenge cognitions are common in early recovery from IPV, Jewish women survivors typically progress toward nonretaliatory coping strategies. Implications Results emphasize the importance of culturally informed therapeutic approaches that acknowledge and support this transformation process.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.047
GPT teacher head0.289
Teacher spread0.242 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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