The Use of Patient Stories as a Knowledge Translation Strategy to Facilitate the Sustainability of Evidence-Based Interventions (EBIs) in Healthcare
Bibliographic record
Abstract
Background: Patient stories are real-life experiences told from a patient’s or their family’s perspective. In the past, patient stories have served many purposes in healthcare, such as spreading knowledge, educating providers, or conveying the patient experience. Patient stories are increasingly used as a knowledge translation (KT) strategy to improve the uptake of evidence-based interventions (EBIs) into clinical healthcare practices by embodying the patient experience. However, little is known about the use of patient stories to support the sustainability of EBIs in healthcare practices. There is a need to understand how patient stories can be used for the long-term use and benefit of EBIs in practice. Objective: Our research explored how patient stories facilitate the sustainability of EBIs in healthcare. Methods: We conducted a secondary thematic analysis of 20 qualitative interviews from a realist evaluation previously published by Flynn et al. Results: We found that the use of patient stories as a KT strategy for the sustainability of two EBIs created buy-in towards new research, motivated and encouraged staff to continue to engage with the intervention long-term and facilitated the spread of the EBI. Our findings demonstrate how sharing patient stories digitally or through learning collaboratives and online toolkits, can facilitate sustainability by enabling patient stories to be saved and distributed to a wide audience at any time. Despite the potential use of patient stories to support long-term research use, more research is needed to understand how effective patient stories are at supporting the long-term use of research evidence aimed to improve healthcare practice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".