"Letting stories breathe": Using Patient Stories for Organizational Learning and Improvement
Bibliographic record
Abstract
There has been a recent upsurge in the use of patient stories to better understand patients’ experiences of illness and of care, and to inspire leaders and staff for quality and safety within healthcare. However, to fully realize the potential of patient stories, a more nuanced understanding is needed of how they are used, who tells them, for what purpose, and in what context. Using a constructivist case study methodology with qualitative methods, this study examined four healthcare organizations that are known leaders in the systematic and deliberate use of patient stories, exploring the storytellers, the types of stories told and their purposes. It also examined the contexts that enable the use of stories and the impact they have had on organizational learning and quality improvement. An interpretivist approach to analysis highlighted the specific types of stories told by patients and of patients, and how they were co-constructed from stories of chaos into quest stories for learning, “authorized stories” to be shared for particular purposes. The storytellers who emerged were those who had extended their involvement as patient advisors/members, determined by leaders to be the “right fit” and at the “right time” to share their stories. Strong leaders modeled and supported the philosophical orientation toward patient and family-centred care that patient stories helped to develop and sustain. Leaders also created the organizational structures and processes required to gather and share stories, and to link them purposefully with learning and improvement. The act of storytelling is not a simple one and tensions surfaced relating to what stories are told, how, by whom, and for what purposes. In many ways, the organizations demonstrated how they were thinking with stories and how learning occurred at individual, team, and organizational levels. However, leaders and organizations continued to retain control of which patient stories were shared, in what forum, and for what purposes. Despite their best intentions and explicit demonstrations to hear the patient voice, a more reflective practice is required to better appreciate the power and privilege that exists within organizations, making this an area to explore further in theory development for organizational learning.
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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 | 0.027 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".