Co-design Process of a Digital Return-to-Work Solution for People With Common Mental Disorders: Stakeholder Perception Study
Bibliographic record
Abstract
BACKGROUND: Service users and other stakeholders have had few opportunities to influence the design of their mental health and return-to-work services. Likewise, digital solutions often fail to align with stakeholders' needs and preferences, negatively impacting their utility. mWorks is a co-design initiative to create a digital return-to-work solution for persons with common mental disorders that is acceptable and engaging for those receiving and delivering the intervention. OBJECTIVE: This study aimed to describe stakeholder perceptions and the involvement of a design process during the prototype development of mWorks. METHODS: A co-design approach was used during the iterative development of mWorks. Overall, 86 stakeholders were recruited using a combination of purposeful and convenience sampling. Five stakeholder groups represented service users with experience of sick leave and common mental disorders (n=25), return-to-work professionals (n=19), employers (n=1), digital design and system developers (n=4), and members of the public (n=37). Multiple data sources were gathered using 7 iterations, from March 2018 to November 2020. The rich material was organized and analyzed using content analysis to generate themes and categories that represented this study's findings. RESULTS: The themes revealed the importance of mWorks in empowering service users with a personal digital support solution that engages them back in work. The categories highlighted that mWorks needs to be a self-management tool that enables service users to self-manage as a supplement to traditional return-to-work services. It was also important that content features helped to reshape a positive self-narrative, with a focus on service users' strengths and resources to break the downward spiral of ill health during sick leave. Additional crucial features included helping service users mobilize their own strategies to cope with thoughts and feelings and formulate goals and a plan for their work return. Once testing of the alpha and beta prototypes began, user engagement became the main focus for greater usability. It is critical to facilitate the comprehension and purpose of mWorks, offer clear guidance, and enhance motivational and goal-setting strategies. CONCLUSIONS: Stakeholders' experience-based knowledge asserted that mWorks needs to empower service users by providing them with a personal support tool. To enhance return-to-work prospects, users must be engaged in a meaningful manner while focusing on their strengths and resources.
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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 | 0.031 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".