Differences in Memory, Perceptions, and Preferences of Multimedia Consumer Medication Information: Experimental Performance and Self-Report Study
Notice bibliographique
Résumé
Background Electronic health resources are becoming prevalent. However, consumer medication information (CMI) is still predominantly text based. Incorporating multimedia into CMI (eg, images, narration) may improve consumers’ memory of the information as well as their perceptions and preferences of these materials. Objective This study examined whether adding images and narration to CMI impacted patients’ (1) memory, (2) perceptions of comprehensibility, utility, or design quality, and (3) overall preferences. Methods We presented 36 participants with CMI in 3 formats: (1) text, (2) text + images, and (3) narration + images, and subsequently asked them to recall information. After seeing all 3 CMI formats, participants rated the formats in terms of comprehensibility, utility, and design quality, and ranked them from most to least favorite. Results Interestingly, no significant differences in memory were observed (F2,70=0.1, P=0.901). Thus, this study did not find evidence to support multimedia or modality principles in the context of CMI. Despite the absence of effects on memory, the CMI format significantly impacted perceptions of the materials. Specifically, participants rated the text + images format highest in terms of comprehensibility (χ22=26.5, P<.001) and design quality (χ22=35.69, P<.001). Although the omnibus test suggested a difference in utility ratings as well (χ22=8.21, P=.016), no significant differences were found after correcting for multiple comparisons. Consistent with perception findings, the preference ranks yielded a significant difference (χ22=26.00, P<.001), whereby participants preferred the text + images format overall. Indeed, 75% (27/36) of participants chose the text + images format as their most favorite. Thus, although there were no objective memory differences between the formats, we observed subjective differences in comprehensibility, design quality, and overall preferences. Conclusions This study revealed that although multimedia did not appear to influence memory of CMI, it did impact participants’ opinions about the materials. The lack of observed differences in memory may have been due to ceiling effects, memory rather than understanding as an index of learning, the fragmented nature of the information in CMI itself, or the size or characteristics of the sample (ie, young, educated subjects with adequate health literacy skills). The differences in the subjective (ie, perceptions and preferences) and objective (ie, memory) results highlight the value of using both types of measures. Moreover, findings from this study could be used to inform future research on how CMI could be designed to better suit the preferences of consumers and potentially increase the likelihood that CMI is used. Additional research is warranted to explore whether multimedia impacts memory of CMI under different conditions (eg, older participants, subjects with lower levels of health literacy, more difficult stimuli, or extended time for decay).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».