“Am I Becoming a Serial Killer?” A Case Study of Cognitive Behavioral Therapy for Mental Illness Anxiety
Bibliographic record
Abstract
BACKGROUND: Although mental illness anxiety is described in the literature, there is very little information on which to draw when treating individuals who present with fears and worries about mental health. In fact, we identified no previous case descriptions focused on this form of anxiety and treated from a cognitive behavioral perspective. AIMS: The current case study aims to advance the understanding of the clinical picture of mental illness anxiety, and facilitate the understanding of how cognitive behavioral techniques for health anxiety can be effectively adapted and implemented for such a case. METHOD: A case study approach was adopted in which a baseline condition and repeated assessments were conducted during an 8-week treatment and 2-month follow-up period. In the current case study, we discuss the assessment, conceptualization, and cognitive behavioral treatment of a 24-year old woman who presented with mental illness anxiety. Several common health anxiety assessment tools and cognitive behavioural techniques were adapted for her particular clinical presentation. RESULTS: Consistent with research evidence for health anxiety, significant improvements in health anxiety and anxiety sensitivity were seen after eight sessions of therapy and maintained at 2-month follow-up. CONCLUSIONS: The results provide preliminary evidence that cognitive behavioral techniques for health anxiety can be effectively and efficiently adapted for mental illness anxiety. However, the lack of available research pertaining to mental illness anxiety contributes to challenges in conceptualization, assessment and treatment.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".