Experiences of Diversity, Inclusion, and Belonging among Postgraduate Health Sciences Research Students at an Australian University: A Qualitative Study
Bibliographic record
Abstract
Postgraduate research students have poorer mental health than the general community. Improving their experiences of diversity, inclusion, and belonging at university may bolster their overall wellbeing and reduce poor mental health outcomes. The aim was to explore postgraduate research students’ views on diversity, inclusion, and belonging, to understand how these experiences impact their mental health and wellbeing, and to identify ways to improve their experiences. Thirty-one postgraduate research students (aged 24-68 years, M = 35.78 years, SD = 10.38; 69% female), enrolled in health sciences degrees at a research-intensive Australian university, completed either an online qualitative survey or participated in a focus group. Content analysis was undertaken to identify core themes. The three main content areas included: diversity (promoting diversity, staff and student training), inclusion (support from supervisors and peers, support in the perinatal period) and belonging (social isolation, suggestions to improve a sense of belonging). Most participants had not received training in diversity, inclusion and belonging, and identified this as an important area of need. Strategies to reduce isolation may potentially improve students experience of inclusion and belonging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".