The Impact of Visualization Format and Navigational Options on Laypeople’s Perception and Preference of Surgery Information Videos: Randomized Controlled Trial and Online Survey
Bibliographic record
Abstract
BACKGROUND: Patients need to be educated about possible treatment choices in order to make informed medical decisions. As most patients are medical laypeople, they find it difficult to understand complex medical information sufficiently to feel confident about a decision. Multimedia interventions such as videos are increasingly used to supplement personal consultations with medical professionals. Former research has shown that such interventions may have a positive effect on understanding, decision making, and emotional reactions. However, it is thus far unclear how different features of videos influence these outcomes. OBJECTIVE: We aimed to examine the impact of visualization formats and basic navigational options in medical information videos about cruciate ligament surgery on recipients' knowledge gain, emotions, attitude, and hypothetical decision-making ability. METHODS: In a between-group randomized experiment (Study 1), 151 participants watched 1 of 4 videos (schematic vs realistic visualization; available vs unavailable navigational options). In a separate online survey (Study 2), 110 participants indicated their preference for a video design. All participants were medical laypeople without personal experience with a cruciate ligament rupture and were presented with a fictional decision situation. RESULTS: In Study 1, participants who used navigational options (n=36) gained significantly more factual knowledge (P=.005) and procedural knowledge (P<.001) than participants who did not have or use navigational options (n=115). A realistic visualization induced more fear (P=.001) and disgust (P<.001) than a schematic video. Attitude toward the surgery (P=.02) and certainty regarding the decision for or against surgery (P<.001) were significantly more positive after watching the video than before watching the video. Participants who watched a schematic video rated the video significantly higher than that by participants who watched a realistic video (P<.001). There were no significant group differences with regard to hypothetical decision making and attitude toward the intervention. In addition, we did not identify any influence of the visualization format on knowledge acquisition. In Study 2, 58 of 110 participants (52.7%) indicated that they would prefer a schematic visualization, 26 (23.6%) preferred a realistic visualization, 17 (15.5%) wanted either visualization, and 9 (8.2%) did not want to watch a video at all. Of the participants who wanted to watch a video, 91 (90.1%) preferred to have navigational options, 3 (3.0%) preferred not to have navigational options, and 7 (6.9%) did not mind the options. CONCLUSION: Our study indicates that the perception of medical information videos is influenced by their design. Schematic videos with navigational options are the most helpful among all videos to avoid negative emotions and support knowledge acquisition when informing patients about an intervention. The visualization format and navigational options are important features that should be considered when designing medical videos for patient education. TRIAL REGISTRATION: Deutsches Register Klinischer Studien DRKS00016003; https://www.drks.de/drks_web/ navigate.do?navigationId= trial.HTML&TRIAL_ID=DRKS00016003 (Archived by WebCite at http://www.webcitation.org/746ASSAhN).
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".