Medical students' perceptions of accessibility and barriers to the utilisation of the University of Pretoria's support systems.
Bibliographic record
Abstract
OBJECTIVE: A campus-based website was set up by medical students following their study that showed a low awareness and utilsation of support systems at the University of Pretoria. The aim of the website was to improve the utilisation of the existing support systems. This study evaluated the students' perception of the support systems and whether the website would resolve barriers hindering utilisation of support. METHOD: Focus groups were selected by theoretical sampling in the first and final quarter of the academic year. The sample group consisted of 120 medical students from all years of study. The transcribed tape recordings, field notes, e-mail responses and observations of the focus groups were analysed for themes by open, axial and selective coding. RESULTS: The medical students varied in their awareness of the available support systems. The majority were unaware of procedures to access the support systems and what services were offered by each. There were numerous barriers to utilising the support systems effectively. The barriers included the students' perception of failure in admitting that they require assistance and the remainder were mostly of an administrative nature. Most students were aware of the website but utilisation was minimal. CONCLUSIONS: The majority of students were aware of but underutilised the support systems for various reasons. The website failed to improve utilisation of the extensive existing support systems. The barriers suggest other ways of improving support that include addressing social support, socialisation and life skills training as well as streamlining the marketing of existing support systems.
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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 | 0.002 | 0.016 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".