Phenomenology of incompleteness and harm avoidance in obsessive-compulsive disorder: An experience sampling study
Bibliographic record
Abstract
This study used experience sampling methodology to explore the phenomenology of the core motivations in obsessive-compulsive disorder (OCD), harm avoidance (HA) and incompleteness (INC), and their influence on the experience of OCD. Fifty participants with a primary OCD diagnosis completed four questionnaires daily for five days about a recent obsessive-compulsive experience and its underlying motivations. A cluster analysis revealed four motivation profiles: high HA/INC, moderate HA/INC, high HA/low INC, and high INC/low HA, with most individuals endorsing a blend of both motivations. On average participants’, HA and INC were stable across the study period. However, participants varied in how their scores changed over time, suggesting potential state-level fluctuations. Both motivations were associated with the interpretation of long-lasting distress related to a particular obsessive-compulsive experience, HA predicted increased beliefs of future harm, and INC was associated with reduced beliefs that the experience meant something negative about themselves. Behaviourally, HA was associated with avoidance, reassurance seeking, and thought suppression, whereas INC was associated with compulsions and reduced likelihood of doing nothing. HA and INC both contribute to how OCD is experienced, although they appear to do so through distinct cognitive and behavioural pathways, offering potential targets for tailored interventions. • Explored harm avoidance and incompleteness OCD motivations with experience sampling. • Four HA/INC profiles were found; most people endorsed a mix of both motivations. • HA and INC were stable overall but showed individual variation in change over time. • HA and INC had unique relationships with OCD interpretations and behaviours. • HA and INC may shape OCD experiences differently, guiding tailored interventions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".