Do Personal Stories Make Patient Decision Aids More Effective? An Update from the International Patient Decision Aids Standards
Bibliographic record
Abstract
BACKGROUND: This article evaluates the evidence for the inclusion of patient narratives in patient decision aids (PtDAs). We define patient narratives as stories, testimonials, or anecdotes that provide illustrative examples of the experiences of others that are relevant to the decision at hand. METHOD: To evaluate the evidence for the effectiveness of narratives in PtDAs, we conducted a narrative scoping review of the literature from January 2013 through June 2019 to identify relevant literature published since the last International Patient Decision Aid Standards (IPDAS) update in 2013. We considered research articles that examined the impact of narratives on relevant outcomes or described relevant theoretical mechanisms. RESULTS: The majority of the empirical work on narratives did not measure concepts that are typically found in the PtDA literature (e.g., decisional conflict). Yet, a few themes emerged from our review that can be applied to the PtDA context, including the impact of narratives on relevant outcomes (knowledge, behavior change, and psychological constructs), as well as several theoretical mechanisms about how and why narratives work that can be applied to the PtDA context. CONCLUSION: Based on this evidence update, we suggest that there may be situations when narratives could enhance the effectiveness of PtDAs. The recent theoretical work on narratives has underscored the fact that narratives are a multifaceted construct and should no longer be considered a binary option (include narratives or not). However, the bottom line is that the evidence does not support a recommendation for narratives to be a necessary component of PtDAs.
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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 | 0.207 | 0.386 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".