Communicating Guideline Recommendations Using Graphic Narrative Versus Text-Based Broadcast Screensavers: Design and Implementation Study
Bibliographic record
Abstract
BACKGROUND: The use of graphic narratives, defined as stories that use images for narration, is growing in health communication. OBJECTIVE: The aim of this study was to describe the design and implementation of a graphic narrative screensaver (GNS) to communicate a guideline recommendation (ie, avoiding low-value acid suppressive therapy [AST] use in hospital inpatients) and examine the comparative effectiveness of the GNS versus a text-based screensaver (TBS) on clinical practice (ie, low-value AST prescriptions) and clinician recall. METHODS: During a 2-year period, the GNS and the TBS were displayed on inpatient clinical workstations. The numbers of new AST prescriptions were examined in the four quarters before, the three quarters during, and the one quarter after screensavers were implemented. Additionally, an electronic survey was sent to resident physicians 1 year after the intervention to assess screensaver recall. RESULTS: Designing an aesthetically engaging graphic that could be rapidly understood was critical in the development of the GNS. The odds of receiving an AST prescription on medicine and medicine subspecialty services after the screensavers were implemented were lower for all four quarters (ie, GNS and TBS broadcast together, only TBS broadcast, only GNS broadcast, and no AST screensavers broadcast) compared to the quarter prior to implementation (odds ratio [OR] 0.85, 95% CI 0.78-0.92; OR 0.89, 95% CI 0.82-0.97; OR 0.87, 95% CI 0.80-0.95; and OR 0.81, 95% CI 0.75-0.89, respectively; P<.001 for all comparisons). There were no statistically significant decreases for other high-volume services, such as the surgical services. These declines appear to have begun prior to screensaver implementation. When surveyed about the screensaver content 1 year later, resident physicians recalled both the GNS and TBS (43/70, 61%, vs 54/70, 77%; P=.07) and those who recalled the screensaver were more likely to recall the main message of the GNS compared to the TBS (30/43, 70%, vs 1/54, 2%; P<.001). CONCLUSIONS: It is feasible to use a graphic narrative embedded in a broadcast screensaver to communicate a guideline recommendation, but further study is needed to determine the impact of graphic narratives on clinical practice.
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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 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".