The structure of obsessionality among young adults
Bibliographic record
Abstract
Although the phenomenology of obsessive-compulsive disorder (OCD) is well understood, less is known about the structure of obsessive symptoms in non-clinical populations. The present study examines the factorial structure of the Leyton Obsessional Inventory short form (LOI-SF) in a sample of 1,015 undergraduate college students. Four factors were extracted describing concerns about contamination (labeled the Contamination factor); repeating behaviors or uncomfortable thoughts or doubts (labeled the Doubts/Repeating factor); checking behaviors, too much attention to detail, honesty concerns, strict conscience and strict routine (labeled the Checking/Detail factor); and taking a long time to dress and to hang up and put away clothing, as well as belief in extremely unlucky numbers (labeled Worries/Just Right factor). Self-report measures of anxiety and ADHD symptoms were correlated positively with these factors, particularly with the checking/detail factor. The prevalence, symptom structure, and patterns of comorbidity seen in this sample of unselected college students are similar to the patterns seen in adolescents with OCD, suggesting that obsessional symptoms and OCD may exist along a continuum.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".