Three Sides to Every Story: Preparing Patient and Family Storytellers, Facilitators, and Audiences
Bibliographic record
Abstract
BACKGROUND: There is a growing recognition that patient engagement is necessary for the cultivation of patient- and family-centered care (PFCC) in the hospital setting. Acting on the emerging understanding that hearing stories from our patients gives valuable insight about our ability to provide compassionate PFCC, we developed an educational patient experience curriculum at our acute care teaching hospital. OBJECTIVES: To understand the benefits and consequences of patient storytelling and to explore the impact of our curriculum on participants. METHODS: The curriculum was codesigned with patients to illustrate the value and meaning of PFCC to health professional audiences. We surveyed audience members at nursing orientation events and interviewed the patient storytellers who shared their stories. RESULTS: Participants indicated that patient stories could serve as lessons or reminders about the dimensions of PFCC and could inspire changes to practice. Storytellers reported an immensely rewarding experience and highlighted the value of educating and connecting with participants. However, they reported that the experience could also pose emotional challenges. CONCLUSION: Careful and considerate facilitation of storytelling sessions is crucial to the delivery of a curriculum that is beneficial to both patients and participants. Our storytelling framework offers a novel approach to engaging patients in education, and it contributes to our existing understanding of how patient engagement efforts resonate within organizations.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".