Comprehensibility of pharmaceutical pictograms: Effect of prospective-user factors and cognitive sign design features

A comparative investigation of machine learning algorithms for predicting safety signs comprehension based on socio-demographic factors and cognitive sign features

This study examines whether the socio-demographic factors and cognitive sign features can be used for envisaging safety signs comprehensibility using predictive machine learning (ML) techniques. This study will determine the role of different machine learning components such as feature selection and classification to determine suitable factors for safety construction signs comprehensibility. A total of 2310 participants were requested to guess the meaning of 20 construction safety signs (four items for each of the mandatory, prohibition, emergency, warning, and firefighting signs) using the open-ended method. Moreover, the participants were asked to rate the cognitive design features of each sign in terms of familiarity, concreteness, simplicity, meaningfulness, and semantic closeness on a 0-100 rating scale. Subsequently, all eight features (age, experience, education level, familiarity, concreteness, meaningfulness, semantic closeness, and simplicity) were used for classification. Furthermore, the 14 most popular supervised classifiers were implemented and evaluated for safety sign comprehensibility prediction using these eight features. Also, filter and wrapper methods were used as feature selection techniques. Results of feature selection techniques indicate that among the eight features considered in this study, familiarity, simplicity, and meaningfulness are found to be the most relevant and effective components in predicting the comprehensibility of selected safety signs. Further, when these three features are used for classification, the K-NN classifier achieves the highest classification accuracy of 94.369% followed by medium Gaussian SVM which achieves a classification accuracy of 76.075% under hold-out data division protocol. The machine learning (ML) technique was adopted as a promising approach to addressing the issue of comprehensibility, especially in terms of determining factors affecting the safety signs' comprehension. The cognitive sign features of familiarity, simplicity, and meaningfulness can provide useful information in terms of designing user-friendly safety signs.

Guessability of standard pharmaceutical pictograms in members of the Nigerian public

Background

Pharmaceutical pictograms are standardized images used to visually convey medication instructions. Very little is known about the ability of Africans to interpret these images.

Objectives

Thus, the aim of this study was to assess the guessability (ability to correctly guess meaning) of selected International Pharmaceutical Federation (FIP) and United States Pharmacopoeia (USP) pictograms in members of the Nigerian public.

Methods

A cross-sectional survey was carried out between May and August 2021 on 400 randomly sampled members of Nigerian public. Selected pictograms (24 FIP and 22 USP pictograms) were grouped and printed on A3 sheets of paper which were used to interview members of the public who fulfilled the study's' eligibility criteria. Respondents were asked to guess the meanings of either the FIP or USP pictograms, and their answers written down verbatim. Descriptive and inferential statistics were used to report the data collected.

Results

Four hundred respondents were interviewed, with 200 respondents each assessing the guessability of the FIP and USP pictograms. The guessability of assessed FIP pictograms ranged between 3.5 and 95%, while that for the USP pictograms was 27.5-97%. Eleven FIP and Thirteen USP pictograms respectively achieved the International Organization for Standardization (ISO) comprehensibility cutoff point of 67%. Guessing performance (the total number of pictograms correctly guessed by an individual) of respondents that assessed the FIP pictograms was significantly associated with their age (p = 0.044) and highest level of education completed (p = 0.003). For the USP pictograms, guessing performance was only significantly associated with the highest educational level completed (p < 0.001).

Conclusions

Guessability of both pictogram types varied widely, but the guessability of the USP pictograms was generally better than that for the FIP pictograms. Many of the tested pictograms may however need to be redesigned before they can be correctly interpreted by members of the Nigerian public.

Development of Self-Management Pictorial Health Information and Comparison of Korean Patients' With Chronic Obstructive Pulmonary Disease and Health Care Professionals' Perceptions

The current study aimed to evaluate pictorial health information (HI) in chronic obstructive pulmonary disease (COPD) patient self-management. Each of 14 literal examples of COPD HI was transformed into three pictorials. The preliminary pictorial COPD HI was validated by 10 experts. In total, 60 patients with COPD and 50 health care professionals (HCPs) were selected to perceive the intended meanings in the pictorial HI. All 42 pictures scored ≥0.8 on the content validity index. Patients chose pictorial HI with descriptions of subjective expressions or those that reflected a patient's actual life, whereas HCPs selected HI that was described in simple, direct, and abstract expressions. Results indicate that HCPs are better suited to provide real life-friendly pictorial HI to patients with COPD. Therefore, it is expected that developing pictograms with patients with COPD could help convey intended meanings. [Journal of Gerontological Nursing, 48(10), 41-46.].

Effects of traffic sign design and personal characteristics on the usability of traffic signs

BACKGROUND: Previous researchers examined the effects of either drivers’ personal characteristics or traffic sign design features on the usability of traffic signs. Their research indicated a connection existed between personal characteristics and usability and between design features and usability. OBJECTIVES: The focus of this study was to investigate which personal characteristics of drivers and which features of traffic sign design affect traffic sign usability the most. METHODS: The study was conducted in three stages. In the first stage, the participants filled out a questionnaire designed to record each driver’s personal characteristics. In the second stage, a System Usability Scale (SUS) was used to evaluate the subjective usability of traffic signs. The SUS had 10 statements that participants (N = 386) scored on a 5-point Likert-type scale from strongly agree to strongly disagree. In the third stage, these participants assigned from 0 to 100 points to 20 signs based on participant perception of the five design features of familiarity, concreteness, simplicity, meaningfulness, and semantic distance. RESULTS: The results showed that four of the five personal characteristics studied (age, education level, possession of a driving license, and formal driving experience) correlated significantly with traffic sign usability. The exception was gender, which did not correlate significantly. Additionally, it was found that the five traffic sign design features correlated in varying degrees of significance with each other and with specific traffic signs. CONCLUSIONS: Traffic sign usability depends mostly both on driver education level and age group and on the design feature of meaningfulness. These findings have implications for how drivers should be trained and how signs should be designed.

Validation of pharmaceutical pictograms among older adults with limited English proficiency

OBJECTIVE

Pictograms on prescription medication labels enhance medication literacy and medication adherence. However, pictograms need to be contextually validated. We assessed the validity of 52 International Pharmaceutical Federation pictograms among 250 older Singaporeans with limited English proficiency.

METHODS

Participants were randomly assigned 11 pictograms each. For each pictogram, participants were first asked its intended meaning. Then, they were told the intended meaning and asked to rate how well the pictogram represented the meaning, on a scale of 1-7. Pictograms were classified as valid (≥66% participants assigned the pictogram interpreted its intended meaning correctly [transparency criterion] and ≥85% participants rated its representativeness as ≥5 [translucency criterion]), partially valid (only transparency criterion was fulfilled) or not valid. Open-ended questions gathered feedback to improve pictograms.

RESULTS

14 pictograms (26.9%) achieved validity and 6 pictograms (11.5%) achieved partial validity. A greater proportion of pictograms for dose and route of administration, and dosage frequency achieved validity or partial validity versus those depicting precautions, indications or side effects.

CONCLUSION

Majority (61.5%) of the assessed pictograms did not achieve validity or partial validity, highlighting the importance of contextual validation.

PRACTICE IMPLICATIONS

Low pictogram comprehension emphasizes the importance of facilitating pictogram understanding during medication counseling.