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Record W2027774068 · doi:10.1080/14733145.2013.770894

The relationship between cognitive errors, coping strategies, and clients' experiences in session: An exploratory study

2013· article· en· W2027774068 on OpenAlexaff
Sara Antunes‐Alves, Katherine Thompson, Uëli Kramer, Martin Drapeau

Bibliographic record

VenueCounselling and Psychotherapy Research · 2013
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyCoping (psychology)CognitionInterpersonal communicationRating scaleClinical psychologyExploratory researchSession (web analytics)PsychotherapistApplied psychologySocial psychologyDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.242
GPT teacher head0.491
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2013
Admission routes1
Has abstractyes

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