Social justice and social media: How medical schools display critical consciousness online
Bibliographic record
Abstract
Academic medical institutions have a pivotal role in addressing the inequalities faced by marginalized populations, especially by promoting values of social justice on online platforms that not only reach the medical sphere, but also the broader public. Central to this transformative agenda is the framework of critical consciousness (CC), which compels individuals to develop an acute awareness of societal inequalities and power dynamics and act as agents of change against inequalities across society. To investigate if and how medical schools use X (formerly Twitter) to display CC, tweets from March 22 - June 22, 2023 from all available Canadian medical school Twitter accounts were obtained and deductively coded. First, a content analysis was performed to collate and categorize tweets related to CC, followed by a critical discourse analysis with a CC framework to examine the role of language in conveying messages about equity and medical education. Of the 3442 tweets reviewed, 554 displayed CC (16.12%). The content analysis revealed that Empowerment of Marginalized Populations was the most prominent display of CC amongst tweets (n = 286), whereas there was a paucity of messaging around Intersectionality (n = 20). The critical discourse analysis revealed that language was purposefully used to positively spotlight equity-deserving individuals (e.g., "celebrate" and "recognize") with minimal dialogue framing institutions as agents of systemic power differentials. Medical schools ultimately advocate for positive change by sharing awareness-raising content that celebrate marginalized communities. However, the step beyond surface-level awareness-raising content towards critical self-reflection that acknowledged institutions' roles in perpetuating inequities was largely limited; this represents a missed opportunity to leverage the power of social media and engage in meaningful dialogue online to build trust between the healthcare sector and the public.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".