Failure to thrive: A QuantCrit analysis of academic failure and everyday discrimination in undergraduate nursing education
Bibliographic record
Abstract
BACKGROUND: Academic failure in nursing education is frequently framed as a student deficit, detached from the structural and institutional forces that shape educational outcomes. This framing obscures how racism, bias, and exclusionary evaluation practices influence who fails-and under what conditions. OBJECTIVE: This study examines the relationship between everyday discrimination and academic failure in undergraduate nursing education, using a Quantitative Critical Race Theory (QuantCrit) lens to foreground identity as a site of structural vulnerability. METHODS: A cross-sectional survey was conducted with 256 undergraduate nursing students at a Canadian university. Descriptive analyses, chi-square tests, t-tests, and logistic regression models were used to examine associations between everyday discrimination, academic failure, and social identity. RESULTS: Students who reported academic failure had higher levels of perceived everyday discrimination. Academic failure was more common among racialized students (54.4 %) compared to non-racialized students (38.5 %). The interaction of race and gender revealed that racialized women had over seven times the odds of academic failure compared to non-racialized men. Skills-based assessments were the most common site of reported failure. CONCLUSIONS: These findings challenge the notion that student failure is solely due to deficits. Instead, they suggest that structural inequalities, particularly those based on race and gender, significantly impact academic outcomes. A QuantCrit perspective redefines failure as a consequence of institutional structures and power dynamics, influencing evaluation practices, faculty development, and equity accountability in nursing 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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".