Reimagining Bias: Making Strange With Disclosure
Bibliographic record
Abstract
ABSTRACT: Academic presentations in health professions continuing professional development (CPD) often begin with a declaration of real or potential conflicts utilizing a three-slide template or a similar standardized display. These declarations are required in some constituencies. The three-slide template and similar protocols exist to assure learners that the content that follows has been screened, is notionally bias free, and without financial or other influence that might negatively affect health provider behavior. We suggest that there is a potential problem with this type of process that typically focusses in on a narrow definition of conflict of interest. There is the possibility that it does little to confront the issue that bias is a much larger concept and that many forms of bias beyond financial conflict of interest can have devastating effects on patient care and the health of communities. In this article, we hope to open a dialogue around this issue by "making the familiar strange," by asking education organizers and providers to question these standard disclosures. We argue that other forms of bias, arising from the perspectives of the presenter, can also potentially change provider behavior. Implicit biases, for example, affect relationships with patients and can lead to negative health outcomes. We propose that CPD reimagine the process of disclosure of conflicts of interest. We seek to expand reflection on, and disclosure of, perspectives and biases that could affect CPD learners as one dimension of harnessing the power of education to decrease structural inequities.
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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 | 0.183 | 0.397 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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".