The Perils of the Unknown: Intolerance of Uncertainty and Intimate Partner Violence Across the First Four Pandemic Waves
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".