A survey of nursing students’ experiences and accessibility to non-educational support services at a regional university campus
Bibliographic record
Abstract
Background and objective: Higher education students face considerable financial hardship during their university life. Many resort to seeking paid employment to subsidise their living arrangements. For a select few this is not always sufficient, especially those from a low socioeconomic backgrounds. Nearly 40% of university students live below the poverty line. This has important implications for academic success and student health and wellbeing. The aim of this study was to review and identify the “non-educational” services nursing students are accessing to support academic success in the undergraduate nursing programme. Setting and methods: A total population sample of 243 nursing students from all three years of the undergraduate nursing programme at a satellite campus in regional Australia were invited to participate in an online 25-item survey. The survey questions consisted of student demographic data and questions addressing access and use of non-standard university services which incorporated multiple choice questions, Likert and open ended questions. Data was analysed using descriptive statistics and inductive content analysis. Results: Student demographics suggest that more than 30% of students in the sample are living below the poverty line, 55% of students come to university hungry, and the majority of students were frequently accessing free foodstuffs. Two themes emerged from the open-ended questions – accessibility and being grateful. Conclusions: Student poverty places students in a very stressful and difficult situation when prioritising between attending university or seeking paid employment. The students in this study identified that food insecurity and financial hardship were major issues that they often experienced on a daily basis despite access to other income streams. The needs for these types of services are growing as students feel the burden of achieving a university education and the debt that accompanies it.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".