CB-FMEA: Adapting the Failure Modes and Effects Analysis to Assess Failures in Community Settings
Bibliographic record
Abstract
This study introduces the Community-Based FMEA (CB-FMEA), a novel adaptation of Failure Modes and Effects Analysis (FMEA) designed to identify system failures in community settings. Traditional FMEA tools overlook contextual and environmental risk factors, such as social, psychological, economic, legal, and technological factors, which significantly impact healthcare access and delivery. Cardiologists were interviewed to explore contextual factors affecting cardiovascular care access and management. Interview data informed a patient journey process map and populated a traditional FMEA table, revealing key failures linked to SDOH (e.g. education, income, stress). Findings highlight the limitations of the conventional FMEA in capturing contextual factors leading to socio-psychological harm. The proposed CB-FMEA adaptation integrates participatory methods, qualitative insights, and a contextual risk assessment. Future research will expand stakeholder participation (e.g., patients, family physicians) and consider how to incorporate field notes into the CB-FMEA process.
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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 | 0.000 | 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 | 0.000 | 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".