Psychometric evaluation and factor analysis of the Iranian version of the Fear of Guilt Scale (FOGS) for obsessive-compulsive disorder (OCD)
Bibliographic record
Abstract
Introduction: Fear of guilt is a characteristic of obsessive-compulsive disorder, which confirms the centrality of guilt in obsessive-compulsive symptoms. Aim: This study aims to investigate the psychometric properties and factor structure of the Iranian adaptation of the Fear of Guilt Scale. Method: This study was applied and developmental in terms of aim and psychometric type,respectively. The population included all the people of the three company in Tehran, Iran, in the year 2022 from which 520 adults, were selected by availabile sampling. The Vancouver Obsessional Compulsive Inventory (2004) and the Fear of Guilt Scale (2016) were used to collect the data. Besides, mean, standard deviation (SD), exploratory and confirmatory factor analysis, convergent validity and construct validity, SPSS version 26 and AMOS version 24 were run to analyze the data. Results: Confirmatory factor analysis revealed the existence of two factors: "punishment" and "prevention of harm." The scale demonstrated favorable convergent validity. Concerning scale reliability, the Cronbach's alpha coefficient was calculated at 0.90 for the entire scale, 0.88 for the "punishment" subscale, and 0.75 for the "prevention of harm" subscale which revealed the convergent and construct validity of this questionnaire. Conclusion: This study confirms the psychometric properties of the Fear of Guilt Scale in terms of evaluating the features of fear of guilt in obsessive-compulsive disorder .Future researches can use this tool for the purposes of evaluating and investigating the fear of guilt in patients with obsessive compulsive disorder.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".