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Record W4413082081 · doi:10.1111/pops.70061

Collective post‐traumatic growth: Validating and measuring positive change in the collective self among victims of sexual violence

2025· article· en· W4413082081 on OpenAlexaff
Orla T. Muldoon, Magdalena Skrodzka, Neela S. Mühlemann, Elayne Ahern, Renate Ysseldyk, Renee St‐Jean, Steve Milton, Nyla R. Branscombe

Bibliographic record

VenuePolitical Psychology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsCarleton University
FundersEuropean Research Council
KeywordsPosttraumatic growthPsychologySocial psychologyConstruct (python library)SolidarityConfirmatory factor analysisCollective behaviorPoliticsDevelopmental psychologyStructural equation modelingSociologyPolitical science

Abstract

fetched live from OpenAlex

Abstract Across the world, women's personal responses to gender‐based violence are increasingly political. In the current paper, we consider whether positive changes in the collective self, arising from personal experience of gender‐based violence, which may lie at the heart of this phenomenon, can be evidenced. Four studies are reported that evidence and validate a proposed construct that reveals collective post‐traumatic growth (PTG) among people who have experienced sexual violence. Confirmatory factor analyses, including a preregistered analysis, indicate that collective PTG is a multidimensional construct and is distinct, yet related to personal PTG. A longitudinal analysis offers evidence of stability and highlights the importance of collective efficacy and group solidarity in determining collective PTG over time. A final experimental study provides evidence of collective PTG determining people's emotional responses to reminders of their trauma. Taken together, these studies emphasize the significance of collective growth as an often overlooked, though positive and important, sociopolitical response to direct experiences of very personal trauma.

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)0.001
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 score0.722
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.073
GPT teacher head0.378
Teacher spread0.305 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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