Medical students as research participants: Student experiences, questionnaire response rates and preferred modes
Bibliographic record
Abstract
Background. Research projects frequently include students, a potentially vulnerable population, as participants.Objectives. To determine University of the Free State (UFS) medical students’ experiences as research participants. Response rates to and preferences for hard copy and electronic questionnaires were also investigated.Methods. All 804 UFS undergraduate medical students in 2020 were approached to participate in this cross-sectional survey. Fourth- and fifth-year students and one-half of the third-year class were approached in person to complete anonymous hard copy questionnaires in a class setting. First- and second-year students and the other half of the third-year class were contacted electronically to complete anonymous electronic questionnaires.Results. Response rates to hard copy questionnaires were at least ~45% of the entire year group (and >70% of those to whom the questionnaire was distributed in class) compared with approximately a third of those contacted electronically. Students who responded to electronic questionnaires preferred electronic completion, whereas those who responded to hard copy questionnaires preferred the hard copy format, except fifth-year students. The majority of students (except those in their first year) had previously been approached to participate in research projects. Between a fifth and a third of all year groups indicated that they had refused participation at least once. More than a third of fifth-year students experienced insufficient time to decide on participation. Up to a quarter of third- to fifth-year groups had felt pressurised to participate.Conclusion. Hard copy questionnaires in class, the preferred data collection method for many students, produced better response rates but placed potential pressure on students to participate.
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 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 | 0.037 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".