Exploring the Impact of Storytelling for Hospitalized Patients Recovering from COVID-19
Bibliographic record
Abstract
There are mental and physical deficits associated with COVID-19 infection, particularly among individuals requiring hospitalization. Storytelling is a relational intervention that has been used to help patients make sense of their illness experiences and to share their experiences with others, including other patients, families and healthcare providers. Relational interventions strive to create positive, healing stories versus negative ones. In one urban acute care hospital, an initiative called the Patient Stories Project (PSP) uses storytelling as a relational intervention to promote patient healing, including the development of healthier relationships among themselves, with families and with healthcare providers. This qualitative study employed a series of interview questions that were collaboratively developed with patient partners and COVID-19 survivors. The questions asked consenting COVID-19 survivors about why they chose to tell their stories and to flesh out more about their recovery process. Thematic analyses of six participant interviews resulted in the identification of key themes along a COVID-19 recovery pathway. Patients' stories revealed how survivors progress from being overwhelmed by their symptoms to making sense of what is happening to them, providing feedback to their care providers, feeling gratitude for care received, becoming aware of a new state of normal, regaining control of their lives, and ultimately discovering meaning and an important lesson behind their illness experience. Our study's findings suggest that the PSP storytelling approach holds potential as a relational intervention to support COVID-19 survivors along a recovery journey. This study also adds knowledge about survivors beyond the first few months of recovery.
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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 | 0.002 |
| 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".