Near misses: Paradoxical realities in everyday clinical practice
Bibliographic record
Abstract
This qualitative study was conducted to define and describe what constitutes and contributes to near miss occurrences in the health-care system and what is needed to ensure safer processes of care. Nine health-care organizations (13 sites total) including six academic health sciences centres (acute care, mental health and geriatric) and three community hospitals participated in this study. The final sample consisted of 37 focus groups (86 in the nursing staff only; 62 in the pharmacy staff only; and 99 in the mixed nursing and pharmacy focus groups respectively) and 120 interviews involving 144 health-care consumers. Data were collected using focus groups (health-care professionals) and key informant interviews (health-care consumers). A multi-level content analyses schema (transcription, coding, categorizing, internal consistency, thematic analysis and community validation) was used. Six themes emerged from the multi-level content analyses that combined focus group (health-care professionals) and key informant interview (health-care consumers) data. These themes are discussed under the three original research questions with supporting data derived from codes and categories. Study findings implicate changes for the health-care landscape relative to system, health policy, professional development and quality improvement.
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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 | 0.027 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".