Building Learning Practices with Self-Empowered Teams for Improving Patient Safety
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Each primary care practice should be viewed as a complex adaptive micro-system with its own unique characteristics. To improve safety, under constraints of limited resources and numerous competing demands, practices need to identify those vulnerabilities that pose the greatest risks and focus efforts on these. The Objective was to develop and test a novel methodology that forms self-empowered learning teams that can prioritise safety problems based on the combination of error frequency and severity of consequences, and then devise feasible interventions. METHODS: A survey instrument was designed and used to elicit, in qualitative terms, staff perceptions of frequency, p, and severity, s, of various types/causes of primary care errors. The qualitative responses were quantified using an algorithm that allowed for risk aversion. Relative hazard rate, h = pxs, was used as the basis for prioritising safety problems in two primary care test practices. RESULTS: Each site identified its own set of priorities with very little overlap. Within each site there was high concordance between priorities identified by physicians, nursing and administrative staff but each site appeared to be unique. Priorities also remained stable with variation in the degree of risk aversiveness assumed in the Hazard calculation. INTERPRETATION AND CONCLUSIONS: The method aided formation of central ‘attractors’ in the form of self-empowered effective learning teams with a common vision to help their complex micro-systems to adapt and thrive. This pro-active type of methodology helps in creating a sustainable safety culture, and has been adapted for other health-care settings and physician training.
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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 | 0.022 | 0.043 |
| 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 | 0.003 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".