Coping Skills Mobile App to Support the Emotional Well-Being of Young People During the COVID-19 Pandemic: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic is likely to increase anxiety and distress in young people worldwide. It is important to prioritize mental health during crisis events to mitigate the negative and often long-term effects of the crises on young people, families, and society. Mental health and well-being apps represent a scalable approach for improving psychological outcomes in young people and have potential to improve the equity of service access. OBJECTIVE: The Whitu: 7 Ways in 7 Days well-being app was recently developed by our group to address the urgent need for innovative approaches to reach young New Zealanders who are struggling to cope with the COVID-19 pandemic. The aim of this study is twofold: to evaluate the acceptability of the prototype app and to examine the effectiveness of the refined app at improving mental and emotional well-being and reducing depression, anxiety, and stress in young people in New Zealand. METHODS: A two-phase mixed methods study will be undertaken to achieve these aims. During the first phase, 20 young people aged 16-30 years (including those of Māori and Pacific ethnicity) will participate in a qualitative study to help refine the prototype app. During the second phase, 90 young people aged 16-30 years will participate in a randomized waitlist-controlled trial (RCT) to evaluate the efficacy of the refined Whitu app at 4 weeks and 3 months after baseline. Outcomes will be evaluated using validated web-based questionnaires at baseline, 4 weeks, and 3 months. RESULTS: The study received ethics approval in May 2020, and recruitment for the focus groups commenced in June 2020. Recruitment for the RCT is expected to commence in October 2020. Participants for both study phases will be recruited via social media and web-based communities. Data collection for the RCT is expected to be completed by January 2021, and analyses are expected to be completed by March 2021. Linear mixed modelling will be used to determine between-group differences in psychological outcomes. CONCLUSIONS: There is an urgent need to develop culturally appropriate, scalable mental health interventions to address the psychological consequences of the COVID-19 pandemic. In this study, we will develop and test an evidence-based well-being app that, if effective, can be made available to all young people in New Zealand and internationally. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ACTRN12620000516987); https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=379597. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/23716.
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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 | 0.041 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.077 | 0.016 |
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".