Impact of Self-Help Schema Therapy on Psychological Distress and Early Maladaptive Schemas: A Randomised Controlled Trial
Bibliographic record
Abstract
Self-help cognitive behaviour therapy has been found helpful in treating anxiety and depression. Recent evidence suggests that self-help schema therapy may represent another treatment alternative. The present study aimed to provide a preliminary assessment of the efficacy of self-help schema therapy on psychological distress and early maladaptive schemas (EMSs) using a 6-week treatment protocol with minimal email contact. Method: Participants were recruited from the general population and randomly assigned to self-help schema therapy ( n = 32) or a waitlist ( n = 32). Intent-to-treat analyses and study completer analyses were conducted using repeated-measures analyses of variance (time × group). Results: Intent-to-treat analyses revealed that treatment produced a marginal improvement in distress, but no change in EMSs. Among study completers ( n = 34), self-help schema therapy yielded large reductions in distress scores on the Outcome Questionnaire-45.2 (partial eta squared = .16). Compared to the waitlist, self-help schema therapy also produced a moderate decrease in EMSs (partial eta squared = .10). The majority of study completers showed reliable clinical change in distress and reported high levels of satisfaction with the intervention. Conclusion: Self-help schema therapy may be an effective treatment for those individuals who persist in treatment. Self-help schema therapy has the potential to help a large number of individuals who may not otherwise have access to services. More research is needed to determine variables associated with treatment adherence and successful outcome.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".