Exploring the Attitudes of Students to the Study of Biosciences within a Traditional, Complementary, and Integrative Medicine Curriculum
Bibliographic record
Abstract
Bioscience subjects are a core component of all health practitioner degrees. While negative attitudes towards biosciences have been explored among nursing and allied health students, little is known about how traditional, complementary and integrative medicine (TCIM) students perceive these subjects. This study employed an explanatory mixed-methods design to investigate the attitudes of first-year TCIM students—within their first or second week of study—towards biosciences in an Australian curriculum. The primary aim was to assess whether attitudes were positive or negative; the secondary aim was to examine the influence of demographic factors. A 41-item Likert scale survey, incorporating affective, cognitive, and perceived control domains, was completed by 586 students between February and December. One-way ANOVA and Tukey-Kramer post hoc tests identified statistically significant differences across demographic variables. While overall attitudes were positive, students aged 35 and older reported higher anxiety (p = 0.003), perceived difficulty (p < 0.001), and self-efficacy (p = 0.035) than younger students. Those who had studied only biology at school reported greater enjoyment (p < 0.001), self-efficacy (p = 0.001), and anxiety (p < 0.001) compared to those with no prior science background. To further explore these results, four open-ended survey questions were analysed thematically (n = 161). Responses revealed both increased confidence and ongoing concerns, particularly around subject relevance and difficulty. These findings suggest that while TCIM students value biosciences, tailored educational strategies are needed to support mature-age learners and those with limited prior science education.
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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 | 0.007 |
| 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) | 0.003 | 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".