Storytelling for impact: the creation of a storytelling program for patient partners in research
Bibliographic record
Abstract
Abstract Storytelling is a powerful means to evoke empathy and understanding among people. When patient partners, which include patients, family members, caregivers and organ donors, share their stories with health professionals, this can prompt listeners to reflect on their practice and consider new ways of driving change in the healthcare system. However, a growing number of patient partners are asked to ‘share their story’ within health care and research settings without adequate support to do so. This may ultimately widen, rather than close, the gap between healthcare practitioners and people affected by chronic disease in this new era of patient and public involvement in research. To better support patient partners with storytelling in the context of a patient-oriented research network, Canadians Seeking Solutions and Innovations to Overcome Chronic Kidney Disease (Can-SOLVE CKD) Network adapted an existing in-person storytelling workshop for patient educators within a hospital setting. The result is a 6-week virtual program called Storytelling for Impact, which guides patients, family members, caregivers and organ donors in developing impactful stories and sharing them at health care and research events, e.g., conferences. The online series of synchronous workshops is co-facilitated by story coaches, who are program alumni and Can-SOLVE CKD staff with trained storytelling experience. Each story follows a structure that includes a call to action, which aims to positively impact the priority-setting and delivery of care and research in Canada. The program has been a transformational process for many who have completed it, and numerous other health organizations have expressed interest in sharing this tool with their own patient partners. As result, we have also created an asynchronous online program that can be used by other interested parties outside our network. Patient partners who share their stories can be powerful mediators for inspiring changes in the health care and research landscape, with adequate structured support. We describe two novel programs to support patient partners in impactful storytelling, which are applicable across all health research disciplines. Additional resources are required for sustainability and scale up of training, by having alumni train future storytellers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".