The impact of emotion regulation therapy on emotion differentiation in psychologically distressed caregivers of cancer patients
Bibliographic record
Abstract
Background and objectives Emotion differentiation is considered adaptive because differentiated emotional experiences are believed to promote access to the information that emotions carry, enabling context-appropriate emotion regulation. In the present study, secondary analyses from a recent randomized controlled trial (O’Toole et al., 2019) were conducted to investigate whether emotion differentiation can improve as a result of psychotherapy and whether improvements in emotion differentiation are associated with reduced distress.Design and methods A total of 81 distressed caregivers of cancer patients were randomized to Emotion Regulation Therapy (ERT), an intervention aimed at improving emotion differentiation and facilitating healthy emotion regulation, or a waitlist condition. Emotion differentiation scores could be calculated for 54 caregivers.Results Repeated measures ANOVAs revealed that ERT led to significant improvements in negative (η2 = 0.21, p = .012), but not positive emotion differentiation (η2 = <0.01, p = .973). Correlation analyses showed that improvements in negative emotion differentiation were not associated with changes in distress.Conclusions The results suggest that negative emotion differentiation can improve as a result of psychotherapy. Further research is needed to clarify how improvements in emotion differentiation following therapeutic interventions relate to treatment outcomes such as distress.Trial registration: ClinicalTrials.gov identifier: NCT02322905.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".