The first year experience of occupational therapy students at an Australian regional university: promoting student retention and developing a regional and remote workforce
Bibliographic record
Abstract
Objective: Student retention at regional universities is important in addressing regional and remote workforce shortages. Students attending regional universities are more likely to work in regional areas. First year experience at university plays a key role in student retention. This study aimed to explore factors influencing the first year experience of occupational therapy students at a regional Australian university. Design: Surveys were administered to 58 second year occupational therapy students in the first week of second year. Data were analysed using descriptive statistics, inferential statistics (Pearson χ2; Spearman rho) and summarising descriptive responses. Setting: An Australian regional university. Participants: Second year undergraduate occupational therapy students. Main outcome measures: Factors influencing students' decisions to study and continue studying occupational therapy; factors enhancing first year experience of university. Results: Fifty-four students completed the survey (93.1%). A quarter (25.9%) of students considered leaving the course during the first year. The primary influence for continuing was the teaching and learning experience. Most valued supports were orientation week (36.7%) and the first year coordinator (36.7%). Conclusion: The importance of the first year experience in retaining occupational therapy students is highlighted. Engagement with other students and staff and academic support are important factors in facilitating student retention. It is important to understand the unique factors influencing students' decisions, particularly those from regional and remote areas, to enter and continue in tertiary education to assist in implementing supports and strategies to improve student retention.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".