Using a Critical FMEA Approach to Identify Equity-Related Failures in Usability Evaluations of Health Technologies
Bibliographic record
Abstract
Design issues that affect the usability of health technologies for marginalized populations may lead to further exacerbating health inequity. To address this concern, we propose the use of failure modes and effects analysis (FMEA) to systematically identify potential inequities that result from usability evaluation methods. This new application of FMEA grounds the traditional FMEA in critical theory and introduces two key concepts: the equity failure mode and reflexivity. To test our approach, we engaged 13 usability practitioners with expertise in healthcare in a series of four workshops. We found that when participants located themselves reflexively in their practice of usability evaluation, they were able to identify more nuanced equity failure modes. Through reflecting on our experience using this method, we aim to illustrate that the critical FMEA is a viable approach human factors practitioners and researchers can use to anticipate equity failure modes in design and evaluation methods.
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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 | 0.207 | 0.309 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.021 | 0.005 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".