Bibliographic record
Abstract
The scholarly publication of patient safety initiatives must contribute more to accelerating reliable, safe patient care. Reports of safety initiatives generally describe specific safety practices and the resulting clinical outcomes. So why is progress so slow to make patients safer?1–3 Do the reported safety practices in such reports in fact lack convincing and plausible supporting evidence?4 Or, do the patient safety practices work, but require more explicit attention to implementation strategies? We suggest “Yes”—to both questions. Moreover, context lies at the heart of the answers to both. The lack of useful focus on context has led to heterogeneity in both evaluation of effective patient safety practices and successful implementation strategies.5–7 In this issue of BMJ Quality & Safety , three papers report a project led by researchers from RAND with a national team of US researchers and international group of technical advisors that investigated the role of context in scholarly patient safety reports.8–10 Together with an earlier paper from the same group,7 they found that few reports actually define context in sufficient detail to offer strategies for replication. They report that most publications omit any empirical assessment of the impact of context on implementation of safety practices.10 They also provide an extensive list of specific contextual elements relevant to patient safety interventions and a typology for organising them.8 9 The shortest definition of context is everything that is not the intervention itself.10 11 In conventional clinical research, this distinction is simple. For example, a medication under study constitutes the intervention. Clinic staff that educate patients about the medication and other infrastructure that enables patients to adhere to their treatment represent elements of context. Quality improvement scholars would agree these elements of context have the makings of a worthwhile intervention. In fact, case …
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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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.017 | 0.057 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.014 | 0.034 |
| Insufficient payload (model declined to judge) | 0.044 | 0.018 |
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".