Pictograms: Can They Help Patients Recall Medication Safety Instructions?
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Résumé
INTRODUCTIONDifficulties in comprehending medication information often lead to medication errors, misinterpretation of instructions and/or symptoms, and lesser self-care behavior (Bains & Egede, 2011; Wolf, Feinglass, Thompson, & Baker, 2010). Hence effective communication of medication information is key in assuring patients' understanding of their medication regimen and the safe and effective use of their medications. Effective communication of medication information is derived from factors stemming from both the patient and the healthcare provider. Patient factors such as knowledge, literacy, numeracy, cognitive skills, language barriers, beliefs on health, specific health conditions, and socio-economic factors (such as social connectedness, access to health care, age, education, immigration status and employment) influence health literacy and, consequently, communication with healthcare providers (Volandes & Paasche-Orlow, 2007). Factors external to the patient that influence health communication effectiveness are the provider's communication skills, complexity of health information, characteristics of the healthcare setting, system demands and expectations upon patients, as well as time pressures upon health care professionals that may limit the building of a relationship with the patient (Makoul, 2001).Unfortunately, too many people do not have the necessary health literacy skills to make informed decisions about their health and do not adequately understand health information received from their healthcare provider (Canadian Council on Learning, 2008; Statistics, 2006). One solution identified to support health literacy is to reduce demands placed on individuals (US Department of Health and Human Services, 2008). Pictograms can be used to simplify the process; they are symbols and pictures often combined with simple text to support verbal and written information to help the transmission of health and medication information and support communication between healthcare providers and patients. It is assumed that humans have a cognitive preference for picture-based rather than textbased information (Peter S Houts, Doak, Doak, & Loscalzo, 2006b); however, research on the impact of using pictograms on comprehension and recall has yielded contradictory results. Indeed, some studies have reported no impact of using pictograms on improving health messages comprehension (Friedmann, 1988; Hardie, Gagnon, & Eckel, 1979; Sansgiry & Cady, 1995; Wogalter, Kalsher, & Racicot, 1992).Recall is the process of retrieving individual words or picture elements from memory and is closely related to comprehension, which is the process of interpreting the meaning of words or pictures to understand their collective meaning (Peter S Houts, Doak, Doak, & Loscalzo, 2006a). Patients with complex medication regimes and with low health literacy levels may have difficulty recalling all verbal instruction from memory (Ley, 1982; Board on Population Health and Public Health Practice, 2013; The Institute of Medicine, 2004). Research shows that patients can only recall 29% to 72% of information they receive, with recall rates decreasing as the quantity of information increases (Sadoski & Paivio, 2000). The aim of the literature review is threefold. First to provide an overview of the literature on the impact of using pictograms to enhance recall of written information. Second to structure the information in such way that research contributions can easily be found and compared to each other. Thirdly to identify challenges and elements that need to be explored in order to further refine knowledge into this area of research.MEASURING PICTOGRAM RECALLThere is no consensus on the best methodology to measure recall of pictograms. However, the literature shows some common elements in the methodology, which includes presentation of information (either study or training phase) followed by a recall test which may take place the same day (immediate/short-term recall) or later (long-term recall). …
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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 découle