Augmentation of Psychotherapy through Alternative Preconscious Priming: A Case Series Exploring Effects on Residual Symptoms
Bibliographic record
Abstract
The current paper describes a case series using a new strategy for facilitating change based on Augmentation of Psychotherapy through Alternative Preconscious Priming (APAP) (1) in the treatment of eight treatment-resistant patients suffering from social phobia or generalized anxiety disorder. The patients had previously only shown a partial response to cognitive behavioral therapy (CBT) despite good treatment adherence. The patients completed APAP using a computerized program, which consisted of three steps during which alternative, more functional thoughts and beliefs relevant to the idiosyncratic difficulties experienced by the patients were formulated. Subsequently, these formulations were recorded and mixed with masking relaxing music, which the patient listened to in a passive state twice daily for 20 min for a period of 8 weeks. This case series aimed to assess the effect and acceptability of APAP using quantitative and qualitative measures administered before, after, and 16 weeks' posttreatment. Results showed a reduction in dysfunctional idiosyncratic thoughts reported by most patients, as well as mild improvements in anxiety and important improvements in quality of life. APAP could be a valuable addition to CBT by facilitating or enhancing cognitive and symptom change. Further studies are needed to confirm these promising results.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".