Beliefs around the causes of inequities and intergroup attitudes among health professional students before and after a course related to Indigenous Peoples and colonialism
Bibliographic record
Abstract
BACKGROUND: Addressing the Truth and Reconciliation Calls to Action on including anti-racism and cultural competency education is acknowledged within many health professional programs. However, little is known about the effects of a course related to Indigenous Peoples and colonialism on learners' beliefs about the causes of inequities and intergroup attitudes. METHODS: A total of 335 learners across three course cohorts (in 2019, 2020, 2022) of health professional programs (e.g., Dentistry/Dental Hygiene, Medicine, Nursing, and Pharmacy) at a Canadian university completed a survey prior to and 3 months following an educational intervention. The survey assessed gender, age, cultural identity, political ideology, and health professional program along with learners' causal beliefs, blaming attitudes, support for social action and perceived professional responsibility to address inequities. Pre-post changes were assessed using mixed measures (Cohort x Time of measurement) analyses of variance, and demographic predictors of change were determined using multiple regression analyses. Pearson correlations were conducted to assess the relationship between the main outcome variables. RESULTS: Only one cohort of learners reported change following the intervention, indicating greater awareness of the effects of historical aspects of colonialism on Indigenous Peoples inequities, but unexpectedly, expressed stronger blaming attitudes and less support for government social action and policy at the end of the course. When controlling for demographic variables, the strongest predictors of blaming attitudes towards Indigenous Peoples and lower support for government action were gender and health professional program. There was a negative correlation between historical factors and blaming attitudes suggesting that learners who were less willing to recognize the role of historical factors on health inequities were more likely to express blaming attitudes. Further, stronger support for government action or policies to address such inequities was associated with greater recognition of the causal effects of historical factors, and learners were less likely to express blaming attitudes. CONCLUSION: The findings with respect to blaming attitudes and lower support for government social action and policies suggested that educational interventions can have unexpected negative effects. As such, implementation of content to address the Truth and Reconciliation Commissions Calls to Action should be accompanied by rigorous research and evaluation that explore how attitudes are transformed across the health professional education journey to monitor intended and unintended effects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".