Power/knowledge: A sociomaterial perspective on a new accreditation process during COVID‐19
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic had significant impacts on many aspects of health care and education, including the accreditation of medical education programmes. As a community of international educators, it is important that we study changes that resulted from the pandemic to help us understand educational processes more broadly. As COVID-19 unfolded in Canada, a revised format of undergraduate medical accreditation was implemented, including a shift to virtual site visits, a two-stage visit schedule, a focused approach to reviewing standards and the addition of a field secretary to the visit team. Our case study research aimed to evaluate the sociomaterial implications of these changes in format on the process of accreditation at two schools. METHODS: We interviewed key informants to understand the impacts, strengths and limitations of changes made to the accreditation format. We used an abductive approach to analyse transcripts and applied a sociomaterial lens in looking for interconnections between the material and social changes that were experienced within the accreditation system. RESULTS: Stakeholders within the accreditation system did not anticipate that changes to the accreditation format would have significant impacts on how accreditation functioned or on its overall outcomes. However, key informants described how the revised format of accreditation reconstructed how power was distributed and how knowledge was produced. The revised format contributed to changes in who held power within each of the programmes, within each of the visiting teams and between site members and visiting team members. As power shifted across stakeholders in response to material changes to the accreditation format, key informants described changes in how knowledge was produced. CONCLUSIONS: Our findings suggest that the most powerful knowledge about any given programme might best be obtained through individualised tools, technologies and voices that are most meaningful to the unique context of each programme. Deliberate attention to how knowledge and power are influenced by the interactions between material and social processes within accreditation may help educators and leaders see the effects of change.
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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 | 0.026 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.040 | 0.108 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".