Building safer systems by ecological design: using restoration science to develop a medication safety intervention
Bibliographic record
Abstract
BACKGROUND: Experts call for stronger safety cultures and transparent reporting practices to increase medication safety in today's strained healthcare environments. The field of ecological restoration is concerned with the effective, efficient, and sustainable repair and recovery of ecosystems that have been degraded, damaged, or destroyed. A study was undertaken to determine whether the lessons of restoration science can be adapted to the study of medication safety issues. METHODS: Working with 26 practitioners, the principles of good restoration were used to design and pilot an innovative multifaceted medication safety intervention. The intervention included focus groups with practitioners, the construction and administration of a research based medication safety inventory, repeat digital photography of environmental safety issues, and targeted environmental modifications. RESULTS: Participants were most concerned about staff education and the physical environment for medication administration. Ward staff used the research to build a healthy reporting culture, introduce regular discussions of near misses, develop education strategies, redesign delivery and storage processes, and renovate the environment. CONCLUSIONS: Members of a busy hospital ward successfully adapted methods of restoration science to study, redesign, and strengthen medication safety practices and ward safety culture within existing resources. Further research will be conducted to test the merits of restoration science for health care.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".