A total of 300 medicine and surgery clinical students completed the survey (170 from the University of Lagos and 130 from Lagos State University) resulting in a 40% response rate (calculated as the number of completed questionnaires divided by the potential number of eligible participants based on the MDCN quota for both colleges). The sociodemographic characteristics of the respondents by knowledge, ability and summary scores are shown in Table1. Respondents were 19 to 39 years old with a median age of 23 (IQR: 2224) and slightly higher females (52.3%). At least a quarter of the respondents were from each level, with the majority from sixth (38.3%) and fifth years (36.3%). Most respondents (63.3%) indicated an interest in a career involving research.
Most respondents (92.0%, n=276) indicated they had heard of at least one of the precision medicine terminologies. The most commonly indicated terminology were Pharmacogenomics (71.0%, n=213) and Genomic Medicine (47.7%, n=143), while the least indicated terminologies were Genome-guided prescribing (19.7%, n=59) and Next Generation Sequencing (18.0%, n=54). Among those who had indicated awareness, the most commonly cited source of knowledge was Lectures (49.6%, n=137), Media (34.4%, n=95) and less commonly Healthcare providers (10.1%, n=28) and Peers (5.1%, n=14).
Knowledge scores of the respondents ranged from 4 to 20, with a median knowledge score of 12 (IQR: 814.5). Respondents were more comfortable about their knowledge of genetic variations predisposing to common diseases (43.3%, n=130) and pharmacogenomics (38.0%, n=114). They were least comfortable about their understanding of basic genomic testing concepts and terminology (29.7%, n=89) and next-generation sequencing (23.3%, n=70). The distribution of responses to knowledge questions is shown in Fig.1.
Distribution of knowledge and ability responses of participants
On univariate analyses, respondents medical school year was significantly associated with their knowledge score (F [2,297]=3.23, p=0.04). Compared to those in their 4th year, students in their 6th year had a 1.54-point lower mean knowledge score (95%CI: -2.83, -0.24; p=0.02) while those in 5th year had a 0.39-point lower mean knowledge score but this was not statistically significant (95%CI: -1.69, 0.92; p=0.56). Students who indicated an interest in a career involving research had a borderline significant 1.03-point higher mean knowledge score compared to those who did not (95%CI: -0.03, 2.08; p=0.06). Age, gender and ethnicity of participants did not show any significant associations with knowledge score of the participants.
After sequentially adjusting for age, gender, and interest in a research career, participants medical school year was significantly associated with knowledge score (F [2, 294]=4.78, p=0.009). Students in their 6th year had a statistically significant 2.16-point lower mean knowledge score than those in their 4th year (95%CI: -3.60, -0.72; p=0.003). After adjusting for age, gender, and interest in a career involving research, each unit increase in medical school year was associated with a statistically significant 1.10-point lower mean knowledge score (F [1,295]=8.97, ptrend = 0.003) [Table2].
The ability scores of the respondents ranged from 4 to 20, with a median score of 11 (IQR: 715). Respondents were more comfortable about their ability to recommend genetic testing options to patients (39.0%, n=117), to a lesser extent, understand genomic test results (30.3%, n=91 and were least comfortable in their ability to make treatment recommendations based on genomic test results (29.3%, n=88) and explain genomic test results to patients (29.3%, n=88). The distribution of responses to ability questions is shown in Fig.1.
On univariate analyses, respondents medical school year was significantly associated with ability scores (F [2,297]=6.26, p=0.002). Compared to students in their 4th year, students in their 5th year had a statistically significant 1.47-point lower mean ability score (95%CI: -2.84, -0.09; p=0. 04) while students in their 6th year had a statistically significant 2.44-point lower mean ability score (95%CI: -3.81, -1.08; p<0.001). In addition, each unit increase in knowledge score was significantly associated with a 0.77-point increase in mean ability score (95%CI: 0.69, 0.86; p<0.001). Age, gender, ethnicity of participants and interest in a career involving research did not show any significant associations.
After multivariate adjustments for age, gender, medical school year, interest in a career involving research and knowledge score, participants knowledge score (: 0.76 95%CI: 0.67, 0.84; p<0.001), and medical school year (F [2,293]=4.67, p=0.01) were independent predictors of ability score. Compared to students in their 4th year, students in their 5th year had a 1.24-point lower mean ability score (95%CI: -2.21, -0.27; p=0.01), and those in their 6th year had a 1.58-point lower mean ability score (95%CI: -2.66, -0.50; p=0.004). After adjusting for age, gender, interest in a career involving research and knowledge score, each unit increase in medical school year was associated with a significant 0.78-point lower mean ability score (F [1,294]=8.06, ptrend = 0.005) [Table3].
The attitude scores of participants ranged from 14 to 40, with a median score of 28 (IQR: 2433). The median score on the openness items was 15 (IQR: 1216). Respondents were more willing to use a patients genetic information to guide decisions in clinical practice (62.0%, n=186), use new types of therapies to help patients (60.0%, n=180), and use genome-guided tools developed by researchers (56.0%, n-168) but were less willing to use genome-guided prescribing in their career when senior physicians were not (41.0%, n=123). The median score on the divergence items was 15 (IQR: 1217). Respondents agreed that research-based genome-guided interventions were clinically useful (79.0%, n=237), were willing to prescribe different medications or doses of drugs (61.0%, n=183), to a lesser extent disagreed that clinicians know how to treat patients based on their genetic information better than researchers (52.0%, n=156), and to a much lesser extent disagreed that clinical experience is more important than using a patients genetic information to make decisions (36.3%, n=109). The distribution of responses to attitude questions is shown in Fig.2.
Distribution of participants responses to attitudes questions
Respondents responses to questions assessing their attitudes towards the adoption of genome-guided prescribing and precision medicine. Section A includes the distribution of responses to openness questions while section B includes the distribution of responses to divergence questions
On univariate analyses, each unit increase in knowledge score of the participants was significantly associated with a 0.14 decrease in mean attitude score (95%CI: -0.26, -0.02; p=0.03). Age, gender, ethnicity, medical school year and interest in a career involving research were not significantly associated with attitude scores. Although the association with knowledge score persisted after adjusting for age and gender, adjusting for medical school year and interest in a career involving research resulted in a trend towards a null association. After maximal adjustment for age, gender, knowledge score, and interest in a research career, students in their 6th year had a significant 1.65-point higher mean attitude score than those in their 4th year (95%CI: 0.75, 3.23; p=0.04). However, medical school year overall was not significantly associated with attitude scores (F [2,293]=2.50, p=0.08). Nevertheless, after maximal adjustment, each unit increase in medical school year was significantly associated with a 0.81-point increase in mean attitude scores (95%CI: 0.02, 1.60; ptrend = 0.04) [Table4]. Likelihood ratio chi-square tests did not reveal any evidence of statistical interaction between knowledge scores and medical school year (X2=2.66, p=0.26).
The distribution of ethical concerns expressed by respondents is shown in Fig.3. More than a quarter of the respondents were worried that genomic information obtained would be misused by government and corporate bodies (35.7%, n=107) and that their application would increase margins between the rich and the poor (34.0%, n=102). A similar proportion were worried that results from tests can affect employability if serious genetic defects are made known to their employers (33.0%, n=99) and that they will lead to insurance discrimination (30.0%, n=90). However, less than a quarter of the respondents felt that precision medicine approaches would lead to ethnic/racial discrimination (12.3%, n=37), and only 8.7% (n=26) of the respondents felt that precision medicine approaches would violate privacy and confidentiality.
Respondents perceptions of ethical concerns and education about Precision Medicine
Most respondents (65.0%, n=195) thought it was important to learn about precision medicine. Only 11.3% (n=34) of the respondents felt that their education had adequately prepared them to practice precision medicine. Only 10.7% (n=32) thought they knew who to ask about genomic testing. Finally, only 10.3% (n=31) of the respondents felt their professors had encouraged the use of precision medicine. The distribution of responses to education items is shown in Fig.3.
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Perceptions of Nigerian medical students regarding their ... - BMC Medical Education
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