Co-design of a patient experience survey for arthritis central intake: an example of meaningful patient engagement in healthcare design
Bibliographic record
Abstract
BACKGROUND: To describe the process of patient engagement to co-design a patient experience survey for people with arthritis referred to central intake. METHODS: We used a participatory design to engage with patients to co-design a patient experience survey that comprised three connected phases: 1) Identifying the needs of patients with arthritis, 2) Developing a set of key performance indicators, and 3) Determining the survey items for the patient experience survey. RESULTS: Patient recommendations for high quality healthcare care means support to manage arthritis, to live a meaningful life by providing the right knowledge, professional support, and professional relationship. The concept of integrated care was a core requirement from the patients' perspective for the delivery of high quality arthritis care. Patients experience with care was ranked in the top 10 of 28 Key Performance Indicators for the evaluation of central intake, with 95% of stakeholders rating it as 9/10 for importance. A stakeholder team, including Patient and Community Engagement Researchers (PaCER), mapped and rated 41 survey items from four validated surveys. The final patient experience survey had 23 items. CONCLUSION: The process of patient engagement to co-design a patient experience survey, for people with arthritis, identified aspects of care that had not been previously recognized. The linear organization of frameworks used to report patient engagement in research does not always capture the complexity of reality. Additional resources of cost, time and expertise for patient engagement in co-design activity are recognized and should be included, where possible, to ensure high quality data is captured.
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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 | 0.157 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".