A Web-Based Intervention to Support a Growth Mindset and Well-Being in Unemployed Young Adults: Development Study
Bibliographic record
Abstract
BACKGROUND: Engaging young adults in the labor market is vital for economic growth and well-being. However, the path to employment often presents setbacks that impact motivation and psychological functioning. Research suggests exploring positive psychology interventions in job-seeking and scaling the delivery of these using technology. However, dropout rates are high for self-administered psychological interventions on digital platforms. This challenge needs to be addressed for such platforms to be effective conveyors of psychological interventions. This study addresses this challenge by exploring user-oriented methods and proposes persuasive features for the design and development of a new web-based intervention targeting young unemployed adults. OBJECTIVE: This study aims to provide an overview of a new positive psychology wise intervention, including its theoretical underpinnings and human-centered design methodology, targeting young, unemployed adults. METHODS: Researchers collaborated with designers, developers, and stakeholders to design a web-based positive psychology intervention that leverages evidence-based wise interventions. Key improvements and adaptations were explored through formative usability testing with 13 unemployed young adults aged between 18 and 25 years (the target population). Qualitative usability testing data were collected, analyzed, and integrated into the ongoing design process as iterative improvements. RESULTS: The result of this study is a modular intervention web application named RØST, designed to align with the user needs and the preferences of the specific end-user group of unemployed young adults. During the project, this application evolved from early concept sketches and prototypes into a developed solution ready for further testing and use. Insights from both end-user feedback and rich user observation gained in the study were used to refine the content and the design. To increase targeted end users' motivation, persuasive design features including praise, rewards, and reminders were added. The web application was designed primarily to be used on mobile phones using text messaging for reminders. The development process included technical and data protection considerations. CONCLUSIONS: This study offers valuable insights into developing psychological or behavioral interventions to support unemployed young adults by documenting the design process and the adaptation and combination of diverse theoretical and empirical foundations. Involving stakeholders and end users in the development enabled relatable content development and resolved potential usability problems. An essential implication is the finding that end-user feedback and insights are crucial in shaping interventions. However, we experienced tensions between the evidence-based interventions and the human-centered design approaches. These tensions were not resolved and highlighted a need for ongoing user motivation support through monetary rewards, which were incorporated into the final web app design.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".