Canadian Nurses' Perceptions of Patient Safety in Hospitals
Bibliographic record
Abstract
The topic of patient safety within the health care system is receiving increasing attention. The Academy of Canadian Executive Nurses conducted a national survey on nurses' perceptions of patient safety, using focus groups from Academic Health Science Centres. Over a three month time frame, 22 organizations, and 33 focus groups comprised of 503 nurses provided responses to six questions regarding patient safety in hospitals. The study was designed as a preliminary fact finding initiative resulting in this descriptive report of the concerns as identified within the focus groups. With each issue identification, they were coded and grouped into 23 themes. Nurses overwhelmingly responded that the health care environment, in which they provide care, presents escalating risk to their patients. In particular, Workload/Pace of Work, Human Resources, Nursing Shortage/Staffing, Restructuring/Bed Closures, Patients/Clients, Systems Issues, Physical Environment and Technology/Specialization were themes emphasized as contributing to increased risk in patient care. Health care leaders must play a key role in developing strategies to address the issues nurses have identified and demonstrate a commitment to controlling the situation. This study encourages research into a more explicit understanding of the issues and identification of strategies to address patient safety in health care.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".