A Structural Model of Relationship Between Disgust Propensity and Fear of Contamination: The Mediating Role of Mental Contamination
Bibliographic record
Abstract
Background: Fear of contamination is one of the complex and powerful fears and is often seen in contamination/washing obsessive-compulsive disorder. Earlier researches have shown that this disorder is related to mental contamination and recent research showed that individuals with the fear of contamination are prone to experiencing disgust. Objectives: This study aimed to investigate the mediating role of mental contamination between disgust propensity and fear of contamination. Methods: The sample consisted of 391 students of Shahed University in Tehran city, Iran. The participants were selected by cluster sampling in November and December in 2015. The tools used were Disgust Propensity and Sensitivity Scale-Revised (DPSS-R), Vancouver Obsessional Compulsive Inventory-Mental Contamination Scale (VOCI-MC), and Padua Inventory (PI). The proposed model was examined by Structural Equation Modeling Modeling (SEM), using Amos-22 software. Baron and Kenny as well as bootstrap methods were used for the analysis of the role of mental contamination as a mediator in this relationship. Results: Goodness of fit indexes indicated that the proposed model had a good fit (GFI = 0.92, AGFI = 0.90, TLI = 0.93, CFI = 0.94 (all > 0.90), and RMSEA = 0.04 (CI (90%) = 0.04-.05). The results showed that disgust propensity caused the fear of contamination both directly (β = 0.35; SE = 0.05) and indirectly (β = 0.16; SE = 0.03) through mental contamination. Conclusion: The findings provided support for the proposed model and showed that disgust propensity played a role in increasing mental contamination which, in turn, leads to fear of contamination. As a result, it would seem that the assessment of disgust propensity and mental contamination is essential to treating the fear of contamination and washing behavior.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".