Research Skills in Thesis Versus Course‐Based Master's Programs
Bibliographic record
Abstract
Master’s programs in the anatomical sciences can be either thesis or course‐based, both providing students with opportunities for research skill development through different degree requirements. However, it is often assumed that skills develop naturally during research experiences, such that students may not be aware of the specific skills that they are developing or the experiences that lead to skill development. Explicit research skill development has many benefits in master’s programs, such as a deeper understanding of learning tasks, and is also important for various careers commonly pursued by anatomy graduates. Using online surveys, this study compared past research skill experiences and future career goals of students in a course‐based Master of Science (MSc) in Clinical Anatomy program and six MSc thesis programs. While this research does not lend itself to a hypothesis, it was predicted that the results would highlight areas for curricular updates based on gaps between students’ current research skill competencies and future goals. The study was based on seven research skills identified by an environmental scan and literature review: communication, problem solving, decision‐making, data collection, data analysis, critical appraisal, and information synthesis. Survey respondents from the MSc thesis (n = 11) and MSc Clinical Anatomy (n = 9) programs were asked about their undergraduate research experiences. Seven MSc thesis students (63.6%) completed an undergraduate honours thesis compared to one MSc Clinical Anatomy student (11.1%), a statistically significant difference in proportions of 0.525 ( p = .028). The frequency of opportunities for the development of communication and data collection skills was significantly higher for students who completed an undergraduate honours thesis (mean ranks = 9.50, 9.67) than for those who graduated from a non‐thesis undergraduate program (mean ranks = 4.86, 4.71), U = 6, 5, z = ‐2.384, ‐2.401, p = .035, .022. As well, the perceived data collection competencies of students who completed an undergraduate honours thesis (mean rank = 9.75) were significantly higher than of students who took part in non‐thesis undergraduate research (mean rank = 4.64), U = 4.5, z = ‐2.491, p = .014. Survey respondents were also asked to identify their career aspirations. The two multinomial probability distributions were equal in the population p = .332, showing no statistically significant differences in proportions between the career goals of MSc thesis and MSc Clinical Anatomy students. That said, the data suggests that MSc thesis students aspired for a career in academia (36.4% versus 25.0%) or industry (27.3% versus 0.0%) more than MSc Clinical Anatomy students, whereas more MSc Clinical Anatomy students aimed for a career in medicine (37.5% versus 27.3%) than MSc thesis students. Taken together, these results reveal the similarities and differences between thesis and course‐based master’s students’ research skill backgrounds and career aspirations, illustrating students’ competencies upon entering their programs and the skills important for their career goals. An understanding of these two factors highlights students’ research skill needs to inform curricular updates and program development in anatomy education, ensuring that students have opportunities to develop the skills needed to succeed in their current and future educational and career pathways.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".