At the mercy of myself: A thematic analysis of beliefs about losing control
Bibliographic record
Abstract
PURPOSE: Concerns about the likelihood, consequences, and meaning of losing control are commonplace across anxiety-related disorders. However, several experimental studies have suggested that individuals without a diagnosis of a mental disorder also believe that they can and will lose control under the right circumstances. Understanding the range of beliefs about the nature and consequences of losing control can help us to better understand the continuum of negative beliefs about losing control. METHODS: The present study used thematic analysis to identify common beliefs about losing control in an unselected sample. Twenty-one participants, half of whom met criteria for at least one anxiety-related disorder, were interviewed about their beliefs about losing control. RESULTS: All 21 participants reported that losing control was possible. Losses of control were defined as multifaceted cognitive-behavioural processes and were seen as negative considering the perceived consequences of the losses. Commonly described consequences were harm to oneself or others, powerlessness, and unpleasant emotions during (e.g., sadness, frustration, and anxiety) and following (e.g., regret, shame, and humiliation) a loss of control. CONCLUSIONS: These results suggest that perceived losses of control are common and that negative beliefs about losing may only become problematic when the losses are personally significant. Further, they offer important insight into what is common among clinical and non-clinical beliefs about losing control and inform how these beliefs might be worth targeting in cognitive and behavioural 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 | 0.035 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".