Collaborating with Young Adults Diagnosed with Schizophrenia: A Participatory Design Study to Shape the Healthcare System
Bibliographic record
Abstract
Introduction: Disengagement from mental health services in young adults with schizophrenia has been associated with dissatisfaction and unmet needs. Striving to improve engagement, we invited service users recently diagnosed with schizophrenia to be co-designers of a smartphone technology that will be responsive to their needs. Aim: This paper reports the first phase of a three-phased participatory design process. The objective was to identify needs of support in young adults recently diagnosed with schizophrenia and to generate ideas of how the needs could be accommodated using smartphone technology. Methods: Participatory design guided the research process and a qualitative approach was used to generate and analyse the data. Data were generated by means of participant observations (n = 45 hours) and interviews (n = 6) with young adults from a first episode psychosis program in Denmark. Findings: Low levels of knowledge and high levels of uncertainties are characteristic of young adults recently diagnosed with schizophrenia, bringing about a vast need of support in order for them to gain power over their new life situation. Our study suggests that the smartphone may be used to foster empowerment by guiding the young adult’s actions in situ, providing comprehensive and easily understood information on the go, allowing for recovery tracking, and notification of mental health changes, providing medication overview and giving easy access to healthcare providers. Conclusion: Young adults recently diagnosed with schizophrenia require comprehensive support in order to become empowered to confidently manage their new life situation. The smartphone holds this potential by offering flexible collaboration and timely access to self-management 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 | 0.049 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".