Theories used by nurses to promote patient safety practices: A scoping review
Bibliographic record
Abstract
Aim: To review available scientific evidence in the literature on theories that have been used to promote patient safety practices in the hospital environment.Methods: Design: A scoping review was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist. Method: the study was conducted between October and November 2020. The following databases were used to identify studies: Medline, Lilacs, BDENF, CINAHL, and Scielo Library. The following search key terms were used: “Patient Safety” AND “Nursing Theory” OR “Nursing Theories”. Thirty-two studies were selected and analyzed, composing the review corpus. The studies were read and critically analyzed, followed by the discussion and interpretation of the results with the presentation of the identified evidence. The analysis was carried out using a descriptive method.Results: The 32 articles that made up the study sample were published between 2006 and 2020 in 14 different countries and indicated the use of 34 different theories.Conclusions: By analyzing the study sample, it was possible to identify nursing theories and theories in other fields of science. Some theories have been applied in the foundation of safe nursing care in different contexts and situations or the analysis and creation of safety culture protocols for healthcare organizations.
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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 | 0.032 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.034 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".