Morally uncertain: the influence of intolerance of uncertainty and perceived responsibility on moral pain
Bibliographic record
Abstract
Background and Objectives Morally horrific events can evoke moral pain and may result in a type of psychological distress known as moral injury (MI). Previous research has hypothesized intolerance of uncertainty (IU; the aversive cognitive and behavioural reaction to uncertainty) may predict MI symptomatology due to its influence on perceived responsibility (PR). As such, we examined the influence of IU and PR on moral emotions associated with vignettes depicting morally stressful events.Method Participants (n = 245) completed the IU-Scale Short-Form, and were randomly assigned to listen and imagine themselves in a series of vignettes depicting grave moral transgressions committed either by the self (self-transgression condition; STC) or others (OTC). Participants provided ratings of moral emotions and PR in response to each vignette.Results Significant positive associations were observed between PR and moral emotions in the STC and OTC. IU’s behavioral subdimension, inhibitory IU, was positively associated with moral emotions in the STC. Inhibitory IU did not moderate the association between PR and moral emotions.Conclusion Future research should further explore the interplay of inhibitory IU, PR and MI. Understanding the behavioral inaction associated with elevated inhibitory IU may be important in mitigating painful moral emotions following self-transgressed moral violations.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".