Covert or connected: Motivations for online and interpersonal reassurance-seeking in OCD
Bibliographic record
Abstract
Interpersonal reassurance-seeking is a common anxiety-management strategy used by individuals with a range of disorders, but can be particularly repetitive and exacting among individuals with obsessive-compulsive disorder (OCD). Although this strategy yields short-term relief, it also comes at a cost, creating frustration and straining relationships. Non-interpersonal sources of reassurance-such as the internet-have been less thoroughly studied, but may offer unique advantages. In this mixed-methods study, we explored the differentiating benefits and drawbacks of interpersonal and online reassurance-seeking for individuals with and without OCD. Participants (n = 62 OCD, n = 58 control) completed an imaginal exposure task, after which they indicated their reassurance source preference (interpersonal vs. online) and their primary reasons for this preference. Participants also answered two general questions about their usual motivations for seeking reassurance from each source at the exclusion of the other. We used content analysis to identify patterns in participants' responses, and quantitative methods to examine the most common motivations as well as any between-group differences. Common reasons for seeking reassurance interpersonally included desires for emotional support and personalization, and these findings did not differ between groups. Common reasons for online reassurance typically involved interpersonal concerns, and participants with OCD were more likely to specifically express concern about the personal consequences of revealing one's worries to others. Motivations related to trust and seeking shared experiences were common for both reassurance types. These findings are discussed in the context of the broader literature on reassurance-seeking and obsessive-compulsive symptomatology.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".