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Record W4401767265 · doi:10.1177/08862605241270064

The Perils of the Unknown: Intolerance of Uncertainty and Intimate Partner Violence Across the First Four Pandemic Waves

2024· article· en· W4401767265 on OpenAlexafffund
Kathryn M. Bell, Diane Holmberg, Zoey A. Chapman

Bibliographic record

VenueJournal of Interpersonal Violence · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsAcadia University
FundersAcadia University
KeywordsDomestic violenceLongitudinal studyPandemicPsychologyDemographyPoison controlSuicide preventionHuman factors and ergonomicsInjury preventionOccupational safety and healthCoronavirus disease 2019 (COVID-19)Developmental psychologySocial psychologyClinical psychologyMedicineMedical emergencySociology

Abstract

fetched live from OpenAlex

Theory suggests that intolerance of uncertainty (IU), a tendency to perceive uncertain events as threatening, may serve as a potential risk factor for increased intimate partner violence (IPV) perpetration; however, few studies have investigated this association, and none have taken a longitudinal approach. We investigated the issue in two longitudinal online investigations (initial N = 282 and 1,118), with time periods ranging from just before the COVID-19 pandemic to the fourth pandemic wave, approximately 1.5 years later. IU was a significant predictor of IPV cross-sectionally, and in the short term longitudinally (i.e., over periods of weeks); however, it did not predict IPV over the longer term (i.e., over periods of months or years). In addition, our longitudinal design allowed assessment of IPV trends across pandemic waves. Physical IPV rates remained low and steady across time. Psychological IPV rates showed an increase in the early days of the pandemic, but then dropped and stabilized, albeit at a somewhat higher rate than pre-pandemic. Study 2 had ample representation of LGBTQ+ respondents and showed that the patterns and processes worked similarly for LGBTQ+ and non-LGBTQ+ individuals.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.331
Teacher spread0.309 · 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.

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

Citations4
Published2024
Admission routes2
Has abstractyes

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