Changes in the Frequency of Actions Associated With Mental Health During Online Treatment: Analysis of Demographic and Clinical Factors
Bibliographic record
Abstract
BACKGROUND: Specific daily actions (eg, goal setting, meaningful activities) are associated with mental health. Performing specific daily actions at a higher frequency is associated with significantly lower baseline symptoms of depression and anxiety, as well as better psychological treatment outcomes for depression and anxiety. OBJECTIVE: This study explored how the frequency of specific daily actions associated with mental health may differ prior to, during, and following treatment according to demographic and clinical characteristics. METHODS: Using a sample of 448 patients from an Australian national digital psychology service, we examined baseline differences in daily action frequency and changes in daily action frequency during a digital psychological treatment according to demographic and clinical subgroups. A total of 5 specific types of daily actions were measured using the Things You Do Questionnaire: healthy thinking, meaningful activities, goals and plans, healthy habits, and social connections. RESULTS: The frequency of daily actions differed according to employment status (largest P=.005) and educational level (largest P=.004). Daily action frequency was lower in those participants with more severe or chronic depression or anxiety symptoms (largest P=.004). Participants reported larger increases in how often they did these daily actions from baseline to midtreatment compared to mid- to posttreatment. Depression duration (P=.01) and severity (P<.001) were associated with differences in how daily action frequency changed during treatment. CONCLUSIONS: The findings of this study support continued research exploring the relationship between daily actions and mental health, how this relationship might differ between individuals, and the clinical potential of supporting individuals to increase the frequency of daily actions to improve mental health.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".