The Association of Perceived Stress with Anxiety-related Symptoms during Use of the MindShift app
Bibliographic record
Abstract
Background: Despite their potential to increase public accessibility for mental health resources, little prior research evaluated factors that may moderate the utility of e-mental health interventions. Research to identify moderators of e- mental health response would guide the development of interventions to meet the needs of presently underserved populations. Objective: The present study extended a North American open label trial of a smartphone-based mental health app (MindShift, Anxiety Canada) designed by psychologists and psychiatrists to increase public accessibility to evidence-based anxiety resources. Specifically, this study examined the role of perceived self- efficacy and helplessness to moderate individuals’ response to use of the MindShift app. Methods: Adults ages 18 to 74 (N = 154) in Canada and the USA reported on perceived self-efficacy and helplessness at a pre-treatment baseline assessment as well as after 8- and 16-weeks of using the app. Participants also reported on functional impairment, anxiety symptoms, and depressive symptoms at baseline and after 2-, 4-, 8-, 12-, and 16-weeks of app use. Results: Participant-reported functional impairment, anxiety symptoms, and depressive symptoms decreased over the 16 weeks that they used the app. Linear change over time in each outcome varied between participants but not as a function of participants’ baseline self-efficacy or helplessness. Self-efficacy increased over the 16-week study; helplessness decreased. Conclusions: Overall, the MindShift app may be a useful, scalable, self-guided resource to augment self-efficacy and helplessness in adults seeking help to manage anxiety and related distress.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".