Estimating implicit and explicit racial and ethnic bias among community pharmacists in Canada
Bibliographic record
Abstract
Background: Bias, whether implicit (unconscious) or explicit (conscious), can lead to preferential treatment of specific social groups and antipathy towards others. When healthcare professionals (HCPs), including pharmacists, act on these biases, patient care and health outcomes can be adversely affected. This study aims to estimate implicit and explicit racial/ethnic bias towards Black and Arab people among community pharmacists in Ontario, Canada. Methods: Community pharmacists participated in a secure, web-based survey using a cross-sectional design that included Harvard's Race and Arab Implicit Association Tests (IATs) to examine bias towards Black and Arab people. Explicit (stated) preferences were measured by self-report. Data were analyzed using descriptive and inferential statistics. Results: The study surveyed 407 community pharmacists, 56.1 % of whom were women with an average age of 46.9. Implicit Association Test (IAT) results showed a statistically significant moderate preference for white people over both Black (mean IAT = 0.41) and Arab people (mean IAT = 0.35). However, most pharmacists explicitly stated that they had no racial/ethnic preference, with 75.7 % expressing a neutral preference between Black and white and 66.6 % neutral between Arab and white. However, a slight preference for white individuals was observed. Demographic factors such as age, place of birth, race/ethnicity, and experience significantly impacted IAT scores. For example, older, Canadian-born, white pharmacists with more experience displayed higher implicit bias scores. A mild correlation was found between implicit and explicit bias, indicating as implicit bias increases, explicit bias tends to become more negative. Conclusions: This study is the first to explore the issue of pharmacist bias in Canada and concentrate on anti-Arab bias. Our findings reveal that Ontario community pharmacists tend to have an unconscious inclination towards white people, which calls for further understanding of this matter. Additionally, we discovered a moderate degree of anti-Arab bias, indicating that studies on other HCPs should consider bias against this social group. Educational interventions are needed to address the implicit biases among community pharmacists in Ontario, Canada. These findings should aim to raise self-awareness of biases, educate about the potential implications of these biases on patient care, and provide strategies to reduce bias.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".