Testing the Effectiveness of a Mobile Smartphone App Designed to Improve the Mental Health of Junior Physicians: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Shift (Black Dog Institute) is the first mobile health smartphone app created to support the mental health of junior physicians. Junior physicians experience demanding work conditions, leading to high levels of psychological distress and burnout. However, they are often concerned about the potential career impacts of seeking mental health support. The confidentiality and ease of access of digital interventions may be particularly suited to address these concerns. The Shift app provides therapeutic and psychoeducational content and strategies contextualized for the specific needs of physicians in training. App content includes information on mental health, help seeking, mindfulness, and common workplace-related concerns of junior physicians. OBJECTIVE: This study aims to test, at scale, the effectiveness of Shift among junior physicians working in Australia using a randomized controlled trial design. The primary aim is to examine whether junior physicians using Shift experience a reduction in depressive symptoms compared with a waitlist control group. The secondary aim is to examine whether the app intervention group experiences improvements in anxiety, work and social functioning, help seeking, quality of life, and burnout compared with the control group. METHODS: A total of 778 junior physicians were recruited over the internet through government and nongovernment medical organizations across Australia, as well as through paid social media advertisements. They were randomly allocated to one of 2 groups: (1) the intervention group, who were asked to use the Shift app for a period of 30 days, or (2) the waitlist control group, who were placed on a waitlist and were asked to use the app after 3 months. Participants completed psychometric measures for self-assessing mental health and wellbeing outcomes, with assessments occurring at baseline, 1 month after completing the baseline period, and 3 months after completing the baseline period. Participants in the waitlist control group were asked to complete an additional web-based questionnaire 1 month after receiving access to the app or 4 months after completing the baseline survey. Participants took part in the study on the internet; the study was completely automated. RESULTS: The study was funded from November 2022 to December 2024 by the New South Wales Ministry of Health. Data collection for the study occurred between January and August 2024, with 780 participants enrolling in the study during this time. Data analysis is underway; the effectiveness of the intervention will be estimated on an intention-to-treat basis using a mixed-model, repeated measures analysis. Results are expected to be submitted for publication in 2025. CONCLUSIONS: To the best of our knowledge, this is the first randomized controlled trial to examine the effectiveness of a mobile health smartphone app specifically designed to support the mental health of junior physicians. TRIAL REGISTRATION: Australia and New Zealand Clinical Trials Registry ACTRN12623000664640; https://tinyurl.com/7xt24dhk. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58288.
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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 | 0.036 | 0.033 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.077 | 0.012 |
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".