The development and validation of the Beliefs About Losing Control Inventory (BALCI)
Bibliographic record
Abstract
Cognitive theory of obsessive-compulsive disorder (OCD) proposes that maladaptive beliefs play a pivotal role in the development and maintenance of symptoms. Clinical reports as well as recent psychometric and experimental investigations suggest that control-related beliefs in OCD may benefit from expansion to include aspects of losing control. However, currently available measures either focus on other facets of control (e.g., sense of control) or do not put emphasis on beliefs about losing control (e.g., beliefs about control over thoughts). The current study aimed to develop and validate the Beliefs About Losing Control Inventory (BALCI), a self-report measure of negative beliefs about losing control, in a sample of undergraduate participants (N= 488). An exploratory factor analysis revealed that the BALCI’s 21 items capture negative beliefs about losing control over one’s thoughts, behaviour, and emotions (Factor 1), beliefs about the importance of staying in control (Factor 2), and beliefs about losing control over one’s body/bodily functions (Factor 3). The BALCI was also found to have good convergent and divergent validity and to be associated with elevated OCD symptoms above and beyond previously identified obsessive beliefs. Theoretical implications and recommendations for the field of cognitive-behaviour therapy are discussed.
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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 | 0.006 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".