The relationship between cognitive errors, coping strategies, and clients' experiences in session: An exploratory study
Bibliographic record
Abstract
Aim: Cognitive errors (CE) and coping strategies (CS) can bear weight on how individuals relate to others and perceive interpersonal relationships. However, there is little research into how clients' erroneous beliefs and maladaptive coping strategies can interfere with the therapeutic process. This study utilised a sample of healthy clients to explore the relationship between their CEs and CSs and their evaluation of therapy. Method: Therapy sessions of undergraduate student clients (N =26) were rated using the Cognitive Error Rating Scale (CERS – 3rd edition), the Coping Patterns Rating Scale (CPRS;), the Session Evaluation Questionnaire (SEQ) and the Session Impact Scale (SIS). Results: Clients who engaged in dichotomous thinking endorsed problem solving less and were more likely to feel unsupported and misunderstood by the therapist. Clients who discounted the positive tended to feel more pressured and judged by therapists. Conversely, those who engaged in problem solving were more likely to find sessions deeper and more valuable as compared to those who reacted to stressful events by submission, escape, or opposition. Implications: Better understanding how and when a client's cognitive errors and coping mechanisms are at play during therapy can help clinicians to address them and intervene appropriately.
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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 | 0.002 | 0.013 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".