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Record W4311843602 · doi:10.5195/ijms.2022.1795

Gender Differences in Attitude and Barriers to Research by Medical Undergraduate Students in Nigeria

2022· article· en· W4311843602 on OpenAlexaboutno aff
Kenechukwu Okwunze, Efosa Peace Iyawe, Ifunanya Prosper Agughalam, Aisha Yahya, Priscilla Awoyomi, Emmanuel Metajuwa-kuda, C A Nwamadiegesi, Mayomikun Olawale, Stephen Chukwuemeka Igwe

Bibliographic record

VenueInternational Journal of Medical Students · 2022
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsLikert scaleMedical educationCurriculumTest (biology)Scale (ratio)Scope (computer science)PsychologyFamily medicineQuarter (Canadian coin)Health careMedicinePedagogyPolitical scienceGeography

Abstract

fetched live from OpenAlex

Background.
 Medical research, one of the pillars of medical education plays a crucial impact in advancing healthcare by improving the diagnosis, treatment, and prevention of illnesses. It is important to ensure that medical students and early career physicians are involved to research outside the curriculum at an early stage of training. This early involvement has been widely known to increase one’s likelihood of building a career in research.
 In Nigeria, the gender composition of research personnel in universities is alarming as less than a quarter are female. There is a need to describe the factors responsible for this imbalance in order to inform stakeholders on where actionable measures can be taken.
 
 Aim
 To examine the gender differences in the attitude towards research, willingness to undertake research, and barriers to research reported by undergraduate clinical students in Nigeria.
 
 Methods
 Six hundred and seventy-two (672) undergraduate medical students in their fourth to sixth years of study in seven selected medical schools across Nigeria completed an electronic survey in August 2022. The survey which was hosted on REDCap was adapted from published works which addressed a similar scope and comprised of 56 items divided into five sections. Gender differences in research experience, willingness to participate in research, attitude towards research and barriers that hinder participation in research were explored using a chi-square test. Variables were collected using a 5-point Likert scale ranging from strongly disagree to agree with a “neutral midpoint” and SPSS version 25 was used in the analysis.
 Results
 Although an equal proportion of male and female students reported voluntary involvement in research, 56.2% of male students and 28.8% of female students perceived research as exciting and enjoyable (p<0.001) and 37.5% of male students vs 47.0 of female students perceived research as being complicated. 
 Male students were more willing to spend more than 3 months on a research project (56.0% vs 42.5%, p<0.001), devote as much time to research as to medical studies (40.1% vs 28%, p=0.002), and to pursue a research-oriented career in the future (49.3% vs 32%, p<0.001).
 Overall, male students reported a higher number of barriers than female students. However, lack of personal interest in research (19.2% vs 26.9%, male vs female students, p=0.011) and insufficient training in research methodology (70.1% vs 81.7%, male vs female students, p=0.009) were reported more by female students. 
 
 Conclusion
 Although there are no gender differences in the composition of students who report prior voluntarily involvement in research, there are gender differences in the attitude and willingness as well as barriers encountered by clinical students to carry out research. Tailored measures should be developed around the peculiar barriers expressed by female medical students.
 
 
 
 
 
 
 
 
 
 
 
 
 

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.057
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.057
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.157
GPT teacher head0.545
Teacher spread0.388 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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