Ecological momentary assessment of emotional processing: An exploratory analysis comparing daily life and a psychotherapy analogue session
Bibliographic record
Abstract
Abstract Background Emotional processing has been studied in psychotherapy as a state‐dependent, sequential process of change. So far, no studies have applied this conceptualisation of emotional processing to the assessments of emotion in daily life. This is particularly important in the light of the pertinence of day‐by‐day fluctuations of emotions for understanding mental health and for monitoring the impact of prevention and psychotherapy programmes. This study examined the internal and ecological validity of a state‐dependent conceptualisation of emotional processing in daily life, in comparison with an experiential‐psychodynamic psychotherapy analogue session. Methods In total, N = 42 university students participated in an experiential‐psychodynamic session, completed symptom measures and responded to a one‐week period of ecological momentary assessment (EMA) using a smartphone. Emotional processing in the session was assessed using the valid observer‐rated measure Classification of Affective Meaning States (CAMS), and emotional responses in daily life were assessed using newly developed theory‐consistent items self‐rated via an interactive smartphone program. Results Internal validity was generally satisfactory across the subscales used in EMA. Correspondence between EMA and in‐session emotional processing was generally low, but specific relationships were found between self‐rated fear, rejecting anger, hurt/grief or loneliness and the observer‐rated productive emotions in the psychotherapy analogue session. Relationships between maladaptive emotional processing and intensity in symptoms were found. Conclusions This is the first study to have examined the validity of a state‐dependent conception of emotional processing in daily life, in direct comparison with a psychological session. We recommend using this assessment schedule to develop or complement integrative prevention or intervention programmes.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".