An assessment of appraisals of dating relationship conflicts and perceptions of appropriate coping strategies with psychologically abusive interactions
Bibliographic record
Abstract
Introduction: Stemming from a stress appraisal and coping perspective, the present investigation developed a methodology for assessing how individuals appraise abusive dating relationship conflicts (Study 1) and the implications of such appraisals for informing coping responses to abusive interactions (Study 2). Methods: Participants ranging in age from 17 to 29 years (Study 1: 102 males, 339 females; Study 2: 88 males, 362 females) completed a survey in which they were presented with a series of 10 scenarios that conveyed relationship conflict cues that were ostensibly aligned with various forms of psychological abuse. Results: abusive. Females were further likely to appraise blatant conflicts as more threatening but at the same time more resolvable. Participants who had encountered abuse in their own intimate relationships were especially likely to appraise conflicts as abusive, threatening and uncontrollable. Such appraisals were associated with greater endorsement of avoidant coping strategies in response to an abusive encounter, irrespective of personal relationship experiences. Discussion: It is suggested that how individuals appraise relationship conflicts may be key to their ability to cope effectively with such encounters or to provide appropriate support to those experiencing psychologically abusive relationships.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".