Patterns of privilege: A total cohort analysis of admission and academic outcomes for Māori, Pacific and non-Māori non-Pacific health professional students
Bibliographic record
Abstract
BACKGROUND: Tertiary institutions are struggling to ensure equitable academic outcomes for indigenous and ethnic minority students in health professional study. This demonstrates disadvantaging of ethnic minority student groups (whereby Indigenous and ethnic minority students consistently achieve academic outcomes at a lower level when compared to non-ethnic minority students) whilst privileging non-ethnic minority students and has important implications for health workforce and health equity priorities. Understanding the reasons for academic inequities is important to improve institutional performance. This study explores factors that impact on academic success for health professional students by ethnic group. METHODS: Kaupapa Māori methodology was used to analyse data for 2686 health professional students at the University of Auckland in 2002-2012. Data were summarised for admission variables: school decile, Rank Score, subject credits, Auckland school, type of admission, and bridging programme; and academic outcomes: first-year grade point average (GPA), first-year passed all courses, year 2 - 4 programme GPA, graduated, graduated in the minimum time, and composite completion for Māori, Pacific, and non-Māori non-Pacific (nMnP) students. Statistical tests were used to identify significant differences between the three ethnic groupings. RESULTS: Māori and Pacific students were more likely to attend low decile schools (27 % Māori, 33 % Pacific vs. 5 % nMnP, p < 0.01); complete bridging foundation programmes (43 % Māori, 50 % Pacific vs. 5 % nMnP, p < 0.01), and received lower secondary school results (Rank Score 197 Māori, 178 Pacific vs. 231 nMnP, p < 0.01) when compared with nMnP students. Patterns of privilege were seen across all academic outcomes, whereby nMnP students achieved higher first year GPA (3.6 Māori, 2.8 Pacific vs. 4.7 nMnP, p < 0.01); were more likely to pass all first year courses (61 % Māori, 41 % Pacific vs. 78 % nMnP, p < 0.01); to graduate from intended programme (66 % Māori, 69 % Pacific vs. 78 % nMnP, p < 0.01); and to achieve optimal completion (9 % Māori, 2 % Pacific vs. 20 % nMnP, p < 0.01) when compared to Māori and Pacific students. CONCLUSIONS: To meet health workforce and health equity goals, tertiary institution staff should understand the realities and challenges faced by Māori and Pacific students and ensure programme delivery meets the unique needs of these students. Ethnic disparities in academic outcomes show patterns of privilege and should be alarming to tertiary institutions. If institutions are serious about achieving equitable outcomes for Māori and Pacific students, major institutional changes are necessary that ensure the unique needs of Māori and Pacific students are met.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".