Using a daily diary for monitoring intrusive memories of trauma: A translational data synthesis study exploring convergent validity
Bibliographic record
Abstract
OBJECTIVE: Intrusive memories are a core feature of posttraumatic stress disorder and have transdiagnostic relevance across mental disorders. Establishing flexible methods to monitor intrusions, including patterns and characteristics, is a key challenge. A daily diary has been developed in experimental settings to provide symptom count data, without the need for retrospective self-report over extended time periods (e.g., 1 week, 1 month). We conducted an exploratory, pre-registered data synthesis investigating convergence between the diary and questionnaire measures of intrusive symptoms long used in clinical practice (Impact of Event Scale, IES, and revised version, IES-R, Intrusion subscale). RESULTS: Utilising datasets using the daily diary from 11 studies (4 real-world trauma studies, seven analogue trauma studies; total N = 578), we found significant positive associations between the diary and IES/IES-R Intrusion subscale. Exploratory analyses indicated that the magnitude of this association was stronger for the IES (vs. the IES-R), and in individuals with real-world (vs. analogue) trauma. CONCLUSION: This study provides first evidence of convergent validity of a daily diary for monitoring intrusions with a widely used questionnaire. A diary may be a more flexible methodology to obtain information about intrusions (frequency, characteristics, triggers, content), relative to questionnaires which rely on retrospective reporting of symptoms over extended timeframes. We discuss potential benefits of daily monitoring of intrusions in clinical and research contexts.
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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 | 0.089 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".