Human-Centered Design of Cadre: A Digital Platform to Support Cardiac Arrest (Co-)Survivorship
Bibliographic record
Abstract
Life after sudden cardiac arrest (SCA) encompasses multifaceted challenges pertaining to the psychological, financial, lifestyle, and cognitive aspects of both the survivor and their family’s recovery. Current health services do not address their complex and evolving needs once they return home, leaving them to navigate (co-)survivorship on their own with limited support. In this study, we describe the process of developing user-centered requirements for a digital healthcare intervention, called Cadre. The goals were to address the limitations of equitably providing services and support to cardiac arrest survivors. In Phase I, user journey mapping and Scenario-Based Design methods, supported by Actor-Network Theory, were used to analyze focus group interview data and develop user personas and problem scenarios. Phase II involved the ideation of application features for Cadre. Phase I results include the emergence of a new theme – every cardiac arrest survivor experience is unique – and the creation of personas and scenarios that reflect this theme. The identified needs (validation, guidance, and lack of emotional support) were translated into a vision for Cadre that will be evaluated via iterative prototype development and usability testing in the future. This project addresses a critical opportunity in the field of SCA survivorship as it not only advances the design of a comprehensive support solution for survivors and their families but also represents a crucial move toward equitable post-discharge care for all.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".