The nature of safety problems among Canadian homecare clients: evidence from the RAI-HC<sup>©</sup>reporting system
Bibliographic record
Abstract
AIM(S): The purpose of this study was to identify the nature of patient safety problems among Canadian homecare (HC) clients, using data collected through the RAI-HC((c)) assessment instrument. BACKGROUND: Problems of patient safety have been well documented in hospitals. However, we have very limited data about patient safety problems among HC clients. METHOD(S): The study methodology involved a secondary analysis of data collected through the Canadian home care reporting system. The study sample consisted of all HC clients who qualified to receive a RAI-HC assessment from Ontario, Nova Scotia and Winnipeg Regional Health Authority for the 2003-2007 reporting period. There were a total of 238 958 cases available for analysis; 205 953 from Ontario, 26 751 from Nova Scotia and 6254 from Winnipeg Regional Health Authority. RESULTS: New fall (11%), unintended weight loss (9%), new emergency room (ER) visits (7%) and new hospital visits (8%) were the most prevalent potential adverse events identified in our study. A small proportion of the HC clients experienced a new urinary tract infection (2%). CONCLUSION(S): Understanding clients' risk profiles is foundational to effective patient care management. IMPLICATIONS FOR NURSING MANAGEMENT: We need to begin to develop evidence about best practices for ameliorating safety risk.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".