Storytelling at board meetings: A case study of co-developing recommendations
Bibliographic record
Abstract
In healthcare, stories shared by patients often provide details and insights into experiences of illness and care. Stories are a way to educate healthcare providers and others to improve care and systems to become more patient and family centred and to better meet patients’ needs and priorities. Telling stories may bring benefits to both storytellers and audience members but also presents risks of harm. A reflective storytelling practice aims to honor stories and storytellers by ensuring there is time to prepare, reflect, learn, ask questions, and engage in dialogue with the storyteller to explore what went well and where there are learning and improvement opportunities. Healthcare Excellence Canada (HEC) is a pan-Canadian health organization focused on improving the quality and safety of care in Canada. HEC commits to engage patients, caregivers, and communities and aims to develop practices and structures to enable engagement activities. At the request of the HEC Board, the Patient Engagement and Partnerships team co-developed recommendations on the process for how best to meaningfully share stories at Board meetings, including stories from those leading, providing, and receiving care. This Case Study outlines the process HEC used to co-develop storytelling recommendations, focusing on a trauma-informed approach to create safe spaces for preparing, learning from and reflecting on stories, to clearly articulate their purpose, and to ensure the locus of control for storytelling rests with the storytellers. This Case Study shares these recommendations and invites other organizations to use these recommendations and/or adapt them within their own context. Experience Framework This article is associated with the Infrastructure & Governance lens of The Beryl Institute Experience Framework (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".