How to do no harm: empowering local leaders to make care safer in low-resource settings
Bibliographic record
Abstract
In a companion paper, we showed how local hospital leaders could assess systems and identify key safety concerns and targets for system improvement. In the present paper, we consider how these leaders might implement practical, low-cost interventions to improve safety. Our focus is on making immediate safety improvements both to directly improve patient care and as a foundation for advancing care in the longer-term. We describe a 'portfolio' approach to safety improvement in four broad categories: prioritising critical processes, such as checking drug doses; strengthening the overall system of care, for example, by introducing multiprofessional handovers; control of known risks, such as only using continuous positive airway pressure when appropriate conditions are met; and enhancing detection and response to hazardous situations, such as introducing brief team meetings to identify and respond to immediate threats and challenges. Local clinical leaders and managers face numerous challenges in delivering safe care but, if given sufficient support, they are nevertheless in a position to bring about major improvements. Skills in improving safety and quality should be recognised as equivalent to any other form of (sub)specialty training and as an essential element of any senior clinical or management role. National professional organisations need to promote appropriate education and provide coaching, mentorship and support to local leaders.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".