Deepening Understandings of Social Accountability Using the Arts and Storytelling
Bibliographic record
Abstract
Background: This project aimed to build an understanding of the lived realities of social accountability. The COVID-19 era offered acontextual lens and time marker. Social accountability is gaining importance as an educational concept. To maximize the value, it is essential to know how the concept is interpreted from multiple stakeholder perspectives. Methods: An arts-integrated methodology called Parallaxic Praxis was used to examine the values, meanings, contexts, and lived experiences of social accountability. Participants made art to express and discuss their conceptions of social accountability, viewing and discussing the subtleties and themes that arose from their own and others’ creations. Individual interviews, focus groups, and postworkshop surveys were utilized to identify themes. Results: Due to the timing of the data collection during the COVID-19 pandemic, interpretations of social accountability centered primarily on the pandemic’s effects on perceptions of daily life as well as core personal beliefs. Most individuals viewed medical schools as a place of opportunity to start learning about social accountability, and the majority of individuals answered “yes” when asked if exploring the idea and action of social accountability through arts-integrated methods was valuable to them. Discussion: If the ultimate goal of medical education is creating physicians who are fit-for-purpose, then social accountability needs to be emphasized and reinforced as an important part of their professional identity. Integrating the arts in data collection is a creative means to conduct this study, as the arts allow for diverse audiences to access active and reflective engagement among participants. As a method of expression, it provided additional unique perspectives on one’s own beliefs regarding social accountability and how it is practiced—and what is expected of a socially accountable health system.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".