Development and psychometric evaluation of the Violation Appraisal Measure (VAM)
Bibliographic record
Abstract
Mental contamination refers to feelings of dirtiness and/or urges to wash that arise without direct contact with a contaminant. Cognitive models propose that this results from “serious, negative misappraisals of perceived violations”. However, the specific violation misappraisals most relevant to mental contamination have yet to be established empirically, in part due to the lack of a comprehensive validated inventory of violation appraisals. Therefore, this study’s aim was to develop and validate such a measure. Items for the new Violation Appraisal Measure (VAM) were developed from qualitative interviews, theoretical models, and previous empirical work. An Exploratory Factor Analysis was conducted in a sample of (n = 300) undergraduate participants, which revealed a four-factor structure: Responsibility/Self-Blame, Permanence, Mistrust, and Self-Worth. The VAM showed excellent internal consistency (α = 0.90), good convergent (r = .50 to .64) and adequate divergent (r = -.01 to .46) validity and was predictive of mental contamination symptoms over and above existing related appraisal measures, ΔF(1,289) = 29.35, p < .001, ΔR2 = 0.06. A Confirmatory Factor Analysis in a second sample of (n = 300) undergraduate students confirmed strong model fit for the four-factor structure of the VAM. The development of the VAM is an important contribution to the search for empirically based cognitive mechanisms in mental contamination and other violation-related sequelae.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".