Instruments Assessing Medication Literacy in Psychiatric Patients and the Caregivers: A Systematic Review

• Abstract
• Introduction
• Objectives
• Methodology
• Search Strategy
• Selection Criteria
• Risk of Bias (Quality) Assessment
• Data Extraction
• Data Synthesis
• Results
• Study Inclusion
• Characteristics of Instruments and Studies
• Methodological Quality of Studies
• Quality of Evidence and Synthesis
• Discussion
• Conclusion
• Authorship
• Funding
• References

Abstract

This systematic review investigates the instruments measuring medication literacy (ML) in psychiatric patients and their caregivers. Despite the critical role of ML in ensuring adherence to medication regimens, especially in populations with mental health conditions, existing instruments lack comprehensive validation of their measurement properties. This review identifies and assesses four instruments designed for psychiatric populations based on COSMIN guidelines. The findings reveal significant gaps in the validity and reliability of these tools. The review underscores the necessity for developing new, robust ML instruments tailored to people with mental illnesses and their caregivers to enhance clinical practice and patient outcomes. The results help to inform future psychiatry research and its clinical applications, promoting better medication management and improving adherence towards overall management in psychiatric care settings.

Introduction

Medication literacy (ML) is defined based on four categories: 1) the knowledge required for optimal and safe medication use, 2) the necessary skills for this use, 3) the format of information and pharmacy services needed, and 4) the outcomes and goals of ML [1]. A More operational definition given is the extent to which individuals can, regardless of the method of delivery, acquire, understand, communicate, compute, and process patient-specific information about their medications to make educated decisions about their health and medications to use them safely and effectively [1]. Hence, ML refers to the capability of individuals to effectively employ information about their medications while taking them. Insufficient ML may lead to suboptimal adherence and misconceptions regarding medication details or instructions, thereby increasing the likelihood of medication errors negatively impacting a patient's health. Inappropriate self-medication among adolescents is reported to be associated with lower ML [2]. Proficiency in ML should extend beyond merely understanding information pamphlets or recognizing recommended medication regimens, encompassing competencies such as comprehending drug quantities, adhering to prescription instructions, and knowing how to respond to missed doses or adverse effects [3] [4] as this will contribute to the optimization and effectiveness of each therapy received by the medication consumer. Individuals with a good understanding of their medications are more likely to experience positive health outcomes. This is particularly important in the management of chronic conditions, where adherence to medication regimens is critical for maintaining stability and preventing complications.

The global health data exchange in 2019 stated that 970 million individuals worldwide, or one in every eight persons, had a mental illness, with anxiety and depressive disorders being the most prevalent [5]. Compared to other patient groups with chronic illnesses receiving primary care, non-adherence rates in psychiatric patients were significantly higher [6]. This demonstrates that medication non-adherence is a serious problem for psychiatric patients because it can worsen their condition and decrease the effectiveness of their therapies. Hence, ML assessment of this population is vital in ensuring that individuals with mental health conditions adhere to their prescribed medication regimens. Compared to the general community and other populations without mental illnesses, the psychiatric patient population faces greater struggle and challenges in health literacy [7]. In the context of medication use, many patients with chronic condition demonstrated poor health literacy [8]. In other words, in a psychiatric population where the cognition function of an individual has decreased, obtaining, analyzing, and utilizing information from the treatment received seemed to be particularly challenging. So, by assessing an individual’s ability, interventions can be developed that can help improve ML and ultimately contribute to better treatment outcomes.

Several studies have reported that patients admitted to hospitals due to mental health issues usually reside with or maintain regular contact with their families after being discharged, and many continue to depend on their families for ongoing care and support throughout their lives [9]. Unlike adult patients, young patients or children are heavily dependent on caregivers for taking their medication. From that perspective, this review acknowledges the importance of ML among caregivers of psychiatric patients also and not just among psychiatric patients only. There are many tools available to assess ML in patients, and these have been reviewed by several studies [10] [11]. The unidimensional 14-item Medication Literacy in Spanish and English assessment tool (MedLitRxSE) [4] stands out as the sole recommended tool for evaluating ML in adult recipients, including their informal caregivers as well [10]. However, no published studies have specifically examined and compared the existing instruments employed to assess ML in psychiatric patients. Hence, the main outcome of this systematic review is to capture a comprehensive overview of ML tools and their properties used among psychiatry patients and/or their caregivers by establishing the most appropriate, valid, and reliable instrument for ML with the best evidence.

Objectives

The objectives of this study were to identify medication literacy tools used to assess ML among psychiatry patients and/or their caregivers and to critically rate, compare, and summarize the measurement properties of existing instruments that assess ML among psychiatric patients and/or their caregivers.

Methodology

Prior to the study initiation, this systematic review protocol was registered with PROSPERO (ID: CRD42024489211). This review was conducted following the guidelines by Consensus-based Standards for The Selection of Health Measurement Instruments (COSMIN) [12] [13] [14] and results are presented in accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines [15]. The research questions for this review were: what medication literacy tools are available to evaluate ML in psychiatry patients and/or their caregivers, and what are their assessment features.

Search Strategy

The keywords of the topics for literature searching and retrieval purposes were identified from the research question. PROSPERO database was initially searched on January 2024 for ongoing or recently completed systematic reviews relevant to the topic. Additionally, previous systematic reviews relevant to ML and psychiatry were also checked to get synonyms or relevant keywords that could be suitable for the search in the databases. Search terms include relevant keywords for all the databases and additional use of MeSH terms for the PubMed database. A preliminary search was done in January 2024 to identify potentially relevant articles for the topic of this review. After performing a preliminary search, the search strategy was tested and adjusted by adding or deleting search terms. A retesting was performed as needed to make sure the search strategy was sensitive enough to find all pertinent research. Structured searches were then conducted in March and April 2024 on PubMed, Web of Science, CINAHL, MEDLINE, and Scopus databases. Full search strategies and the results of searches are provided in Supplementary Data S1. The following keywords were utilized in various combinations, employing BOOLEAN operators to refine the searches for the two main topics (medication literacy and psychiatry patients): “medication literacy”, “pharmacotherapy literacy”, “medication knowledge”, “medication education”, “pharmacy health literacy”, “mental health”, “mental disorders”, “mental illness”. Details on the searches for each database are outlined in Supplementary Data S1. All articles obtained from searches were integrated into EndNote20 software for deduplication and management of citations. Citations and abstracts of studies were extracted and documented in a Microsoft Excel spreadsheet after deduplication for further screening and full-text articles were downloaded for screening and assessments based on eligibility criteria.

Selection Criteria

The inclusion criteria for the selection of the articles to be included in this review were: published studies between 2018 to 2022 that aimed to develop or implement ML tools and/or to assess its measurement properties. Measurement properties of an instrument were identified based on the taxonomy by COSMIN guidelines, which include validity, reliability, and responsiveness [12] [13] [14] . Any type of instrument such as a questionnaire, general or specific measures, and any type of method of administration (self-reported, performance-based, etc.), were included in this review. Publications of tools developed to assess ML only if they were in English language and the study focused on any type of psychiatric patients and/or their caregivers irrespective of the type of medication (i. e., prescribed or non-prescribed).

Any publications in which the evaluation of at least one of the measurement properties was not reported were excluded from this review. Since the focus of this systematic review was on psychiatric patients and/or their caregivers, studies that were designed to measure ML in healthcare professionals were not considered. Two reviewers (DFAM, NAB) screened the titles and abstracts of the identified articles against the eligibility criteria independently. Next, reviewers independently review downloaded full texts of potentially relevant articles retrieved based on the inclusion criteria. Doubts between reviewers were resolved after consulting with a third reviewer. The study selection process followed the PRISMA flow diagram [15].

Risk of Bias (Quality) Assessment

Each selected study was subjected to methodological quality assessment using a standardized COSMIN risk of bias checklist [13] [14] by two reviewers independently, and a consensus was reached through discussion with the third reviewer. For each study, the risk of bias for each measurement property was evaluated and scored on a 4-point scale (very good, adequate, doubtful, inadequate). Instrument development, content validity, structural validity, internal consistency, cross-cultural validity, construct validity, test-retest reliability, measurement error, and responsiveness were all covered by the COSMIN risk of bias checklist, but only four out of ten criteria were applicable for selected studies based on available information. The “worst score counts” principle was the basis for the overall ranking. The COSMIN criteria were then used to score the requirements for good measurement qualities as either sufficient (+), insufficient (−), inconsistent (±), or indeterminate (?), resulting in an overall rating of an instrument's quality. Separate ratings for relevance, comprehensiveness, and comprehensibility of content validity were given based on information contained in the studies.

Data Extraction

Data extraction on the studies selected for inclusion was carried out independently by the reviewer. For all included studies, data on specific details about instruments and study population characteristics were extracted and documented in tabular form. The characteristics of the included instrument, such as the name, type, target population, administration method, number of items, response options, range of scores, completion time, and their language, are reported. The reported features of the included study population were: instrument used, study design, country, study population and setting, sample size, mean age, gender, mean duration of study, treatment or diagnosis or years of experience, score distribution, response rate, and other pertinent study population characteristics. Additionally, the ML construct of the study was also compiled, and data regarding domains explored by each instrument were also extracted through subjective assessment by the reviewer using the existing definition of ML by Pouliot et al. [1].

Data Synthesis

Finally, the quality of evidence for all measurement properties of instruments was graded as high, moderate, low, or very low using the modified approach of GRADE. When there were methodological flaws or risk of bias in the studies or there were inconsistencies between the study results and the researchers' subjective assessment of the instrument (content validity) or when the sample size was too small (relevant to internal consistency and reliability), the quality of the evidence was downgraded. The grading was done by two reviewers in consultation with the supervisor. The data synthesized provided recommendations for the most suitable instrument for use based on the COSMIN guidelines, which are divided into three categories: the most suitable instrument to assess ML, an instrument that needs further validation studies, and the instrument not recommended. Overall findings of the quality per measurement properties for each instrument were reported in tabular form, including the grade of the evidence.

Results

Study Inclusion

The literature searches and study selection process are detailed in the PRISMA flow diagram in [Fig. 1]. A total of 727 articles were initially identified after searching five different databases (PubMed, Web of Science, CINAHL, MEDLINE, and Scopus). Out of 727 articles, 156 duplicates were removed using EndNote20 software, and 571 articles were included for title and abstract screening. Only 37 articles were selected for full-text screening according to inclusion criteria. At the full-text screening stage, five duplicates not detected by EndNote20 software were removed manually and eight articles focusing on different objectives than assessing ML level were excluded. Several instruments used for measuring ML were detected; however, they were also excluded since the ML assessment was done on different populations, such as healthcare professionals, non-psychiatric patients, studies that lacked measurement properties or no full text was available. Finally, only four studies reporting four different instruments were included for methodological quality assessment.

Characteristics of Instruments and Studies

A total of four instruments were detected and recorded for this systematic review which were: Medication Knowledge Questionnaire (MKQ) [16] , the 15-item Survey on Knowledge and Comfort with Psychotropic Medication (SKCPM) [17], Patient Knowledge of Antipsychotics (PKA) [18], and Understanding of Medication Questionnaire (UMQ) [19]. These four instruments could be accessed through Open Access or a university subscription. Characteristics of the four instruments are presented in [Table 1] and [Table 2]. Out of four instruments, three of them were designed to assess ML specifically regarding antipsychotic medications [16] [18] [19] meanwhile, only the 15-item SKCPM [17] was designed to assess ML regarding psychotropic medications, encompassing a wider range of medications. The instruments were designed to assess ML of schizophrenia patients [18] [19], patients with psychotic disorder [16] or direct care staff of individuals with intellectual disabilities [17]. The administration method chosen for the instruments was performance-based outcome measures by doing interviews [16] [18] [19] and self-reported outcome measures by answering online surveys [17]. Instruments were composed of 6 to 15 items ranging from different domains. The item-response type for three instruments was not specified in the study except UMQ [19], which employed a 3-to-5-point Likert Scale. None of the instruments used a recall period because the assessment was based on real-time scenarios. Completion time was specified only for two instruments, which ranged from 10–20 min [17] [19]. The ML domains that were explored differed depending on the instruments. The 15-item SKCPM [17] covered the most domains, while the MKQ [16] covered the fewest domains, as shown in [Table 2]. All included studies used different definitions of ML or medication knowledge, some of which were even incomplete or not clearly defined. No included instrument referred to a conceptual framework or a theory when designing their instrument.

The characteristics of the included study population are detailed in [Table 3]. All four included studies were cross-sectional and provided some information regarding the evaluation of the instruments. Three studies were conducted in Asia [16] [18] [19], while only one in North America [17]. Sample sizes ranged from 105 to 365 adults, with the mean age reported in the range of 31.4 to 42.9 years [16] [18] [19]. The mean duration of illness ranged from 10 to 12.4 years [18] [19], and one study reported a sample of patients with a mean treatment duration of 64.8 months [16]. One study focused on caregivers involving direct care staff, with a mean experience of 11 years [17].

Methodological Quality of Studies

All studies reported on the validity of the instruments and only one study additionally reported on the internal consistency and reliability of the instrument [19]. The findings per instrument are summarized in [Table 4]. The quality of instrument development was inadequate for three of the instruments because they did not report any cognitive interview or pilot study for their instrument, except the 15-item SKCPM [17], which was presumed to have tested the instrument and undergone modifications based on feedback. However, their procedures were not clearly described, leading to a doubtful rating. For content validity studies, all instruments demonstrated overall poor methodology, as validity studies only involved the professionals or did not provide sufficient information about their method of validation. UMQ [19] additionally reported on internal consistency value; however, due to the absence of structural validity studies, the internal consistency was devalued, resulting in a doubtful rating. The reliability of UMQ [19] had poor methodological quality as the statistics used (Pearson’s coefficient) were considered doubtful based on COSMIN risk of bias criteria.

Quality of Evidence and Synthesis

Ratings of measurement properties, together with the quality of evidence, are summarized in [Table 4]. The content validity, taken together with the development process and the reviewer’s subjective rating for most of the instruments, showed overall satisfactory results for relevance and comprehensibility but less satisfactory for their comprehensiveness. Regarding the comprehensibility of all instruments involved, despite showing satisfactory results, the evidence was very low because no measurement quality studies were obtained from the study, and only subjective ratings of the reviewer based on the available information about the instrument in the studies were involved. The comprehensiveness of MKQ [16]—considering that the instrument covered a limited domain, which is only to obtain and understand the information about their medication—resulted in an unsatisfactory rating after adding the reviewer’s subjective comments. This rating was supported by moderate evidence, considering the doubtful risk of bias in the study. Other results also were supported by moderate or very low evidence depending on their risk of bias rated as being doubtful or inadequate studies. For the UMQ [19], the information for the comprehensiveness of the instrument was not reported; combined with the reviewer’s subjective rating of the instrument’s available data in the study, which missed some domains of ML, the comprehensiveness was rated as unsatisfactory, supported by very low evidence. The internal consistency of the UMQ [19] reported satisfactory results; however, this was supported with low evidence due to the risk of bias as the methodological rating employed in the study was considered doubtful. As for the reliability of UMQ [19], the result remained indeterminate, as the statistics used were considered insufficient. Detailed information on reasons for downgrading the evidence rating is presented in Supplementary Data S2.

Discussion

In this systematic literature review, four tools were identified to evaluate ML in psychiatric patients and/or their caregivers, regardless of the kind of medication being used or the setting in which it was used. To the best of our knowledge, this is the first study to comprehensively evaluate the methodological quality of ML instruments with a focus on populations in psychiatry. This review was conducted according to the standardized COSMIN methodology criteria.

Based on the results and evidence obtained from the COSMIN criteria assessment, all the instruments have the potential recommendation to be used for assessing ML in psychiatric patients and/or their caregivers in their current states; however, they require supporting studies to further investigate their quality. The 15-item SKCPM [17] and PKA [18] showed satisfactory results for content validity and covered a broad content of domains compared to the other instruments, providing a robust basis for further psychometric analysis of this instrument. The absence of cognitive interviews or pilot testing and other factors may have contributed to the poor content validity of the instruments. Complementary studies should include professionals or members of the target population, respectively, with a particular focus on the comprehensibility of the instrument to improve the quality of the content validity. MKQ [16] and UMQ [19], which provided unsatisfactory results for content validity, would require further revision and content validity studies before being used for any assessment. With respect to UMQ [19], despite having satisfactory internal consistency, content validity, which is considered to be the most important quality of an instrument [12] [13] [14], should be a focus in further studies, and the reliability study is also recommended.

Most of the included studies remain conceptually narrow and primarily focus on their assessment of the ability to obtain, understand, and process the information of their medication. Meanwhile, only a few instruments assess the ability to communicate, calculate dosage, and take appropriate action to effectively manage their medication. This underlines that ML lacks a robust theoretical foundation. Neiva Pantuzza et al. [20] proposed that measuring ML should involve four main dimensions: functional literacy, communicative literacy, critical literacy, and numeracy. These dimensions encompass specific subdimensions such as understanding, accessing, communicating, evaluating, and calculating, which collectively form the foundation for comprehending medication information and achieving the goal of ML [20]. Therefore, components like “dosing information,” “medication name,” and “processing and acting upon received medication instructions” are considered most important [20]. Among the included studies, the MKQ [16] identifies medication knowledge as the awareness of medication information, hence, it does not assess other aspects aside from the ability to find and understand the information. While the MKQ [16] effectively measures patients' basic awareness and understanding of medication information, highlighting important knowledge gaps, its focus is limited, as it does not assess practical skills, behavioral aspects, or communication abilities related to medication management. To comprehensively evaluate ML, the MKQ [16] should be supplemented with tools that test practical skills, problem-solving, and communication with healthcare providers involving the four dimensions recommended [20]. This broader approach ensures a more comprehensive understanding of patients' capabilities to manage their medications safely and effectively. Thus, it seems that in the future development of ML instruments, it is important for researchers to justify their choice of content domains as well as their administration methods based on a clear rationale [10].

The general definition of ML by Pouliot et al. [1] was commonly referred to as operational and comprehensive. However, considering the requirements and specific tasks involved in properly and effectively taking medications, applying a single, uniform definition of ML to all populations and healthcare settings is inappropriate. ML is influenced by a range of external and internal factors [20]. External factors include socioeconomic status, educational background, family and social support, and access to clinical pharmacy services. For instance, individuals with higher earnings and better education may have more resources and knowledge to understand and manage their medications effectively; furthermore, family and social support can provide additional assistance in medication management, and access to clinical pharmacy services ensures professional guidance. Internal factors, such as age and cognitive functions, also play a crucial role [20]. For example, older adults may face challenges with motor abilities or sensory impairments, making medication administration more difficult; cognitive functions are essential for understanding and following medication instructions. These factors are particularly significant in the psychiatric population, as individuals with mental illnesses often experience variations in cognitive functions, which can significantly affect their ability to comprehend and manage their medications. This can be supported by the fact that people with mental illnesses tend to have higher rates of low health literacy compared to the general population [21]. This low health literacy can lead to misunderstandings about medication usage, increased risk of non-adherence, and potential adverse effects. Although we have identified these four potential instruments for assessing the ML of this population, we believe that a good ML instrument should cover all relevant domains and adopt specific information related to the psychiatric population in the future. Hence, it is necessary to further validate these identified instruments in clinical settings involving the target population to assist clinicians in promoting proper medication use among patients.

Conclusion

In conclusion, this systematic review highlights a lack of validated ML instruments designed for psychiatric populations and/or their caregivers. None of the existing tools have been fully validated for all measurement properties aside from content validity. Three instruments were administered to psychiatric patients and one instrument was used to assess the medication knowledge of direct caregivers. Although most instruments showed their potential to be used as they cover most of the relevant domains, further validation studies and retesting are needed. This review suggests developing new instruments that address a broad spectrum of psychiatric illnesses with specific ML tools and satisfactory measurement properties.

Authorship

WFHWMJ and IAW designed the research, analyzed and interpreted the data, and wrote the manuscript. I.A.W assisted in writing of the manuscript, and had primary responsibility to check the final content. DFAM, NAB, and NS helped in data validation, interpretation and assisted in manuscript writing. All authors read and approved the final manuscript.

Funding

Ministry of Higher Education Malaysia for funding this project through the Fundamental Research Grant Scheme (FRGS) (FRGS/1/2022/SKK16/UM/03/1).

Conflict of Interest

The authors declare that they have no conflict of interest.

Acknowledgements

The authors would like to acknowledge the Ministry of Higher Education Malaysia for funding this project through the Fundamental Research Grant Scheme (FRGS) (FRGS/1/2022/SKK16/UM/03/1).

• References

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Correspondence

Publication History

Received: 20 July 2024

Accepted after revision: 07 April 2025

Article published online:
04 June 2025

© 2025. Thieme. All rights reserved.

Georg Thieme Verlag
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• References

• 1 Pouliot A, Vaillancourt R, Stacey D. et al. Defining and identifying concepts of medication literacy: An international perspective. Res Social Adm Pharm 2018; 14: 797-804
  PubMedSearch in Google Scholar
• 2 Lee C-H, Chang F-C, Hsu S-D. et al. Inappropriate self-medication among adolescents and its association with lower medication literacy and substance use. PLoS One 2017; 12: e0189199
  CrossrefPubMedSearch in Google Scholar
• 3 Yeh Y-C, Lin H-W, Chang EH. et al. Development and validation of a Chinese medication literacy measure. Health Expect 2017; 20: 1296-1301
  PubMedSearch in Google Scholar
• 4 Sauceda JA, Loya AM, Sias JJ. et al. Medication literacy in Spanish and English: Psychometric evaluation of a new assessment tool. J Am Pharm Assoc 2012; 52: e231-e240
  PubMedSearch in Google Scholar
• 5 World Health Organization. Mental disorders. 2022 Available from https://www.who.int/news-room/fact-sheets/detail/mental-disorders
  PubMedSearch in Google Scholar
• 6 Ayre MJ, Lewis PJ, Keers RN. Understanding the medication safety challenges for patients with mental illness in primary care: A scoping review. BMC Psychiatry 2023; 23: 417
  PubMedSearch in Google Scholar
• 7 Degan TJ, Kelly PJ, Robinson LD. et al. Health literacy in people living with mental illness: A latent profile analysis. Psychiatry Res 2019; 280: 112499
  PubMedSearch in Google Scholar
• 8 Rahman FI, Aziz F, Huque S. et al. Medication understanding and health literacy among patients with multiple chronic conditions: A study conducted in Bangladesh. J Public Health Res 2020; 9: 1792
  PubMedSearch in Google Scholar
• 9 Hsiao C-Y, Van Riper M. Research on caregiving in Chinese families living with mental illness: a critical review. J Fam Nurs 2010; 16: 68-100
  PubMedSearch in Google Scholar
• 10 Gentizon J, Hirt J, Jaques C. et al. Instruments assessing medication literacy in adult recipients of care: A systematic review of measurement properties. Int J Nurs Stud 2021; 113: 103785
  PubMedSearch in Google Scholar
• 11 Mafruhah OR, Huang Y-M, Shiyanbola OO. et al. Ideal instruments used to measure health literacy related to medication use: A systematic review. Res Social Adm Pharm 2021; 17: 1663-1672
  PubMedSearch in Google Scholar
• 12 Prinsen CAC, Mokkink LB, Bouter LM. et al. COSMIN guideline for systematic reviews of patient-reported outcome measures. Qual Life Res 2018; 27: 1147-1157
  CrossrefPubMedSearch in Google Scholar
• 13 Mokkink LB, de Vet HCW, Prinsen CAC. et al. COSMIN Risk of Bias checklist for systematic reviews of patient-reported outcome measures. Qual Life Res 2018; 27: 1171-1179
  CrossrefPubMedSearch in Google Scholar
• 14 Terwee CB, Prinsen CAC, Chiarotto A. et al. COSMIN methodology for evaluating the content validity of patient-reported outcome measures: A Delphi study. Qual Life Res 2018; 27: 1159-1170
  CrossrefPubMedSearch in Google Scholar
• 15 Page MJ, McKenzie JE, Bossuyt PM. et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021; 372: n71
  Search in Google Scholar
• 16 Lau KC, Lee EH, Hui CL. et al. Demographic correlates of medication knowledge in Hong Kong early psychosis patients. Early Interv Psychiatry 2018; 12: 107-112
  PubMedSearch in Google Scholar
• 17 Tan YM, Chong CP, Cheah YC. Impact of hospital pharmacist-led home medication review program for people with schizophrenia: A prospective study from Malaysia. J Appl Pharm Sci 2019; 9: 034-341
  PubMedSearch in Google Scholar
• 18 Sawyer A, Lake J, Lunsky Y. Direct care staff knowledge about medication for individuals with intellectual disabilities. Adv Ment Health Intellect Disabil 2019; 13: 144-151
  PubMedSearch in Google Scholar
• 19 Issa EM, Hamdan-Mansour AM, Hamaideh SH. Knowledge of and adherence to antipsychotic medications among individuals diagnosed with schizophrenia. Jordan Med J 2019; 53: 113-126
  PubMedSearch in Google Scholar
• 20 Neiva Pantuzza LL, Nascimento ED, Crepalde-Ribeiro K. et al. Medication literacy: A conceptual model. Res Social Adm Pharm 2022; 18: 2675-2682
  PubMedSearch in Google Scholar
• 21 Degan TJ, Kelly PJ, Robinson LD. et al. Health literacy of people living with mental illness or substance use disorders: A systematic review. Early Interv Psychiatry 2021; 15: 1454-1469
  PubMedSearch in Google Scholar

• Supplementary Material