Advancing Allyship Through Anti-Oppression Workshops for Public Health Students: A Mixed Methods Pilot Evaluation
Bibliographic record
Abstract
This pilot mixed methods evaluation describes the impact of an anti-oppression workshop on allyship development among a group of public health graduate students. After completing a mandatory anti-oppression workshop, a survey including closed- and open-ended questions was administered to 41 public health students specializing in health promotion. Closed-ended questions gathered basic demographic data and Likert-type scale responses to assess changes in participant knowledge, awareness, and attitudes surrounding anti-oppression concepts discussed during the workshop, while open-ended questions asked respondents to reflect on how such changes might influence their development as allies. A response rate of 65.85% (27 respondents) was achieved. The majority of the study group were between the ages of 20 and 24 years (74.07%), self-identified as straight (77.8%), and self-identified as non-White (77.8%), while almost the entire group identified as female (92.59%). Five key themes emerged from a directed content analysis of qualitative data, identifying the importance of anti-oppression workshops for allyship development: conducive environments, positionality, knowledge, active listening and learning, and advocacy. These themes were used to construct a mixed methods joint display for comparative interpretation of quantitative and qualitative data. Mixed methods analysis revealed that anti-oppression workshops can promote allyship development by increasing knowledge of key terms and concepts associated with anti-oppression and facilitating critical reflections on power, privilege, and social location. Our findings demonstrate a profound need for ongoing anti-oppression training among future public health students and professionals.
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 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 | 0.073 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".