Adapting co-design methodology to a virtual environment: co-designing a communication intervention for adult patients in critical care
Bibliographic record
Abstract
BACKGROUND: Research co-design is recommended to reduce misalignment between researcher and end-user needs and priorities for healthcare innovation. Engagement of intensive care unit patients, clinicians, and other stakeholders in co-design has historically relied upon face-to-face meetings. Here, we report on our co-design processes for the development of a bundled intensive care unit patient communication intervention that used exclusively virtual meeting methods in response to COVID-19 pandemic social distancing restrictions. METHODS: We conducted a series of virtual co-design sessions with a committee of stakeholder participants recruited from a medical-surgical intensive care unit of a community teaching hospital in Toronto, Canada. Published recommendations for co-design methods were used with exclusively virtual adaptations to improve ease of stakeholder participation as well as the quality and consistency of co-design project set-up, facilitation, and evaluation. Virtual adaptations included the use of email for distributing information, videos, and electronic evaluations as well as the use of a videoconferencing platform for synchronous meetings. We used a flexible meeting plan including asynchronous virtual methods to reduce attendance barriers for time-constrained participants. RESULTS: Co-design participants included a patient and a family member (n = 2) and professionally diverse healthcare providers (n = 9), plus a facilitator. Overall, participants were engaged and reported a positive experience with the virtually adapted co-design process. Reported benefits included incorporation of diverse viewpoints in the communication intervention design and implementation plan. Challenges related to lack of hands-on time during development of the co-designed intervention and participant availability to meet regularly albeit virtually. CONCLUSIONS: This report describes the methods, benefits, and challenges of adapting in-person co-design methods to a virtual environment to produce a bundled communication intervention for use in the adult intensive care unit during the COVID-19 pandemic. Adapting recommended co-design methods to a virtual environment can provide further opportunities for stakeholder participation in intervention 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 | 0.091 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".