Using the experience-based design (EBD) approach to strengthen patients’ impact.
Bibliographic record
Abstract
69 Background: The Experience Based Design (EBD) approach uses patient and clinician experiences to identify opportunities for improvement in the healthcare system. The EBD approach elicits subjective and personal patient, carer, and staff experiences at crucial points in the care pathway by encouraging them to share their stories. Methods: Cancer Care Ontario (CCO), an agency that oversees cancer services in Ontario, held an EBD workshop with the objectives of capacity building and facilitating healthcare improvements throughout the province. 110 participants (27 teams) from across Ontario attended the workshop to engage participants to take an active role in developing actionable plans to address patient experience issues. An evaluation following two years of EBD was necessary to: (a) determine EBD progress and effectiveness; (b) identify successes/challenges for getting projects off the ground; and (c) identify additional resources required to spread EBD across Ontario. The evaluation consisted of: 1) two province-wide electronic surveys (long survey for those directly involved in EBD projects; short survey for frontline staff) and 2) semi-structured phone interviews with patients/caregivers. Results: Some EBD projects have completed multiple initiatives; others are just beginning. Projects address process improvement (e.g., streamlining patient bookings), resource/tool development (e.g., symptom screening tools) and establishing patient advisory boards and committees. Five (28%) survey respondents said that EBD projects elicited implementation of 6 to 10 changes and 6 (38%) respondents indicated that: (1) respect for patient preferences and (2) communication, information and education were two principles of Person-Centred Care (PCC) that improved the most. Conclusions: Future steps include development of a collaborative website, a symposium to showcase projects, an evaluation of the EBD initiative and peer-reviewed publication.
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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 | 0.039 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".