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Record W4408085002 · doi:10.2196/66461

Co-Designed Online Training Program for Worry Management: The Role of Young People With Lived Experience of Worry in Program Development

2025· article· en· W4408085002 on OpenAlexvenueno aff
Jessica Steward, Michelle L. Moulds, Colette R. Hirsch

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsWorryPreprintPsychologyMedical educationTraining (meteorology)Applied psychologyMedicineComputer scienceWorld Wide WebAnxietyPsychiatryGeography

Abstract

fetched live from OpenAlex

Background: Many young people report high levels of worry, highlighting the need for interventions that teach strategies to help them shift focus away from worry. To maximize uptake by this population, interventions should be brief and accessible; to maximize dissemination, they should have potential for delivery at scale. We produced a multisession, online training program, Shift Focus, co-designed with young people with lived experience of worry. The online training program was accessed via a mobile app. In this paper, we describe how Lived Experience Advisory Panel (LEAP) members were involved in each stage of the process of developing the Shift Focus online training program, from refining session content through to designing and testing the online training program prototype. Objective: We aimed to engage with young people with lived experience of worry, to help refine, further develop, and tailor a new online training program designed to help shift focus away from worry. Methods: We recruited LEAP members (aged 16-25 y) with lived experience of worry from diverse backgrounds across the United Kingdom. We used a highly iterative participatory design process, such that LEAP members provided input during all 4 phases of program development: refining and further developing session content, piloting sessions, developing user experience design, and testing the online training program prototype. Results: Feedback from LEAP members during each phase of the online platform development informed key decisions regarding the platform content, functionality, and the interface design to ensure it suited our target population. In phase 1, we learned that the platform needed to be simple and aesthetically pleasing, personalized to individual needs and preferences, accessible to all, track progress, and provide individuals with a sense of community with others with similar lived experiences. In phase 2, we learned that the platform also needed to provide further guidance on how to apply the Shift Focus techniques to daily life, using personalized reminder settings. In phase 3, we additionally learned that ease of navigation and interactivity were key to maintaining user engagement. The importance of program tracking was reiterated, as well as the need for accessibility settings to support all learning styles. In phase 4, we identified that technical problems with the online platform were a barrier to engagement. The inclusion of future iterations (eg, reward systems) to help promote engagement was suggested by LEAP members in multiple phases. Conclusions: LEAP members brought unique expertise and made key contributions to the development of the Shift Focus online training program and were highly valued members of the team. A highly iterative participatory design process enabled continuous feedback from LEAP members throughout, ensuring that their input was meaningful and that their key messages and ideas were incorporated into the final program.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.112
GPT teacher head0.516
Teacher spread0.403 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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