“I just have to take it” – patient safety in acute care: perspectives and experiences of patients with chronic kidney disease
Bibliographic record
Abstract
BACKGROUND: Frequent hospitalizations and dependency on technology and providers place individuals with chronic kidney disease (CKD) at high risk for multiple safety events. Threats to their safety may be physical, emotional, or psychological. This study sought to explore patient safety from the perspectives and experiences of patients with CKD in acute care settings, and to describe willingness to report incidents utilizing an existing safety reporting system. METHODS: This study was conducted using a qualitative interpretive descriptive approach. Face to face interviews were conducted with 30 participants at their bedside during their current hospital admission. The majority of the participants were 50 years or older, of which 75% had a confirmed diagnosis of end stage renal disease with the remainder at stages 3 or 4 of CKD. Eighty percent of the participants were either on hemo- or peritoneal dialysis. RESULTS: Participants expected to receive safe care, to be taken care of, and to be cared for. Safety threats included: sharing a room with patients who were on precautions; lack of cleanliness; and roommates perceived to be threatening. The concepts of being taken care of and being cared for constituted the safety threats identified within the interpersonal environment. Participants felt taken care of when their physical needs are met and cared for when their psychological and emotional needs are met. There was a general lack of awareness of the presence of a safety reporting system that was to be accessible to patients and families by telephone. There was also an overall unwillingness to report perceived safety incidents, although participants did distinguish between speaking up and reporting. CONCLUSIONS: A key finding was the unwillingness to report incidents using the safety reporting system. Fear of reprisals was the most significant reporting impediment expressed. Actively inviting patients to speak up may be more effective when combined with a psychologically safe environment in order to encourage the involvement of patients in patient safety. System-wide organizational changes may be necessary to mitigate emotional and physical harm for this client population.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| 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".