{"id":1039311,"date":"2023-11-24T02:48:55","date_gmt":"2023-11-24T07:48:55","guid":{"rendered":"https:\/\/www.immortalitymedicine.tv\/perceptions-of-nigerian-medical-students-regarding-their-bmc-medical-education\/"},"modified":"2024-08-17T16:26:18","modified_gmt":"2024-08-17T20:26:18","slug":"perceptions-of-nigerian-medical-students-regarding-their-bmc-medical-education","status":"publish","type":"post","link":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/pharmacogenomics\/perceptions-of-nigerian-medical-students-regarding-their-bmc-medical-education.php","title":{"rendered":"Perceptions of Nigerian medical students regarding their &#8230; &#8211; BMC Medical Education"},"content":{"rendered":"<p><p>    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.  <\/p>\n<p>    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).  <\/p>\n<p>    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.  <\/p>\n<p>            Distribution of knowledge and ability responses of            participants          <\/p>\n<p>    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.  <\/p>\n<p>    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].  <\/p>\n<p>    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.  <\/p>\n<p>    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.  <\/p>\n<p>    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].  <\/p>\n<p>    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.  <\/p>\n<p>            Distribution of participants responses to attitudes            questions          <\/p>\n<p>            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          <\/p>\n<p>    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).  <\/p>\n<p>    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.  <\/p>\n<p>            Respondents perceptions of ethical concerns and            education about Precision Medicine          <\/p>\n<p>    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.  <\/p>\n<p><!-- Auto Generated --><\/p>\n<p>Read the original here:<br \/>\n<a target=\"_blank\" href=\"https:\/\/bmcmededuc.biomedcentral.com\/articles\/10.1186\/s12909-023-04841-w\" title=\"Perceptions of Nigerian medical students regarding their ... - BMC Medical Education\" rel=\"noopener\">Perceptions of Nigerian medical students regarding their ... - BMC Medical Education<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p> 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%).  <a href=\"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/pharmacogenomics\/perceptions-of-nigerian-medical-students-regarding-their-bmc-medical-education.php\">Continue reading <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"limit_modified_date":"","last_modified_date":"","_lmt_disableupdate":"","_lmt_disable":"","footnotes":""},"categories":[1246862],"tags":[],"class_list":["post-1039311","post","type-post","status-publish","format-standard","hentry","category-pharmacogenomics"],"modified_by":null,"_links":{"self":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts\/1039311"}],"collection":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/comments?post=1039311"}],"version-history":[{"count":0,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts\/1039311\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/media?parent=1039311"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/categories?post=1039311"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/tags?post=1039311"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}