Failure Mode and Effects Analysis: A Tool for Identifying Risk in Community Pharmacies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
CANADIAN HEALTH CARE LEADERS HAVE BEGUN TO LOOK AT SAFE practices in other industries to identify those with applicability to health care. A key characteristic of high-reliability industries, such as nuclear power, aviation, automobile manufacturing, and chemical processing, is acceptance of the fact that errors will occur, that the impact of errors can be devastating, and that efforts should be made to discover system weaknesses before harm occurs. A tool that has been a cornerstone of safety efforts in these organizations is a proactive risk assessment process called failure mode and effects analysis (FMEA). Using FMEA, multidisciplinary teams first identify potential failures and their effects, and then develop strategies for improvement. FMEA focuses on how and when a system will fail, not if it will fail. The US Veterans Affairs (VA) National Center for Patient Safety has developed an FMEA model for health care environments called Healthcare Failure Mode and Effect Analysis (HFMEA). 1 As part of its role in the Canadian Medication Incident Reporting and Prevention System, the Institute for Safe Medication Practices Canada (ISMP Canada) has adapted the VA model to develop a similar FMEA framework for use in Canada.
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.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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 it