Measuring and improving patient safety culture: still a long way to go
Bibliographic record
Abstract
Patrick Waterson. Published by Ashgate Publishing Ltd. 2014. ISBN: 978-1-4094-4814-3 Patient safety culture (PSC) has become a hot topic within the healthcare safety community. PSC papers now appear by the hundreds; hospitals across the USA and Canada are mandated to survey culture; and numerous translations and validations in other countries have occurred. However, it is important to consider whether we are really gaining anything from all this surveying. PSC surveys have been compared with ‘describing the water to a drowning man’.1 That is, although PSC surveys are instrumental in helping organisations to identify opportunities to improve safety, they typically do not inform solutions. Plus, sometimes it does feel like we are drowning in PSC surveys. PSC: Theory, Methods and Applications , edited by Patrick Waterson, examines the field of PSC and highlights the pervasive use of surveys and questionnaires in the current landscape. In contrast to other high-risk industries, healthcare researchers and practitioners seem to have embraced only a small subset of methods that could be used to measure PSC. PSC is commonly defined according to the following nuclear industry definition: “The safety culture of an organisation is the product of individual and group values, attitudes, perceptions, competencies, and patterns of behaviour that determine the commitment to, and the style and proficiency of, an organisation's health and safety management”.2 Using this definition, which describes values, attitudes and perceptions, and competencies and patterns of behaviour, it appears the healthcare industry has only focused on measuring the first half of the definition (ie, values, attitudes and perceptions) but has neglected measurement of the second half (ie, competencies and patterns of behaviour). PSC surveys and questionnaires have been invaluable for subjectively measuring and raising awareness about PSC. However, it seems we are missing an important opportunity to objectively measure competencies and patterns of behaviour that determine PSC in healthcare. Competencies and …
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.015 | 0.033 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.038 | 0.026 |
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".