Writing Yourself Well: Dispositional Self-Reflection Moderates the Effect of a Smartphone App-Based Journaling Intervention on Psychological Wellbeing across Time
Bibliographic record
Abstract
Abstract Self-reflection is often viewed positively; paradoxically, however, it is also associated with distress, potentially because of its relationship with rumination. Focusing self-reflection on positive themes may be one way to promote adaptive self-reflection. This study examined whether the disposition to engage in self-reflection motivates use of a journal containing positively focused writing prompts and moderates the benefit gained from it, specifically when rumination is controlled for. For 28 days, participants ( N = 152) accessed an app-based mental health intervention containing various features, including the aforementioned journal. Outcomes of self-regulation and psychological wellbeing were assessed, controlling for time spent using other app features. As expected, journaling was associated with improvements in psychological wellbeing but only when baseline self-reflection was average or higher. Journaling was also initially associated with improvements in self-regulation, but this was diminished after controlling for time spent using other app features. Findings suggest self-reflection could be a strength for fostering wellbeing when it is directed in a positive way.
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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 | 0.012 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".