Bibliographic record
Abstract
Patient safety research has focused almost exclusively on hospitals, with few studies investigating the safety of other healthcare sectors, including home care.Before measuring patient safety in home care, this study first sought to translate hospital-focused patient safety definitions and concepts to home care.A contextappropriate approach to measuring adverse events (AEs) in home care was developed using chart reviews prompted by a mixed screening process.These methods were then applied to measure the incidence, type, severity, cause, preventability and ameliorability of AEs among Winnipeg Home Care clients.Adverse Events among Winnipeg Home Care Clients Keir G. Johnson Identifying Patient Safety Risks in Non-Acute Care Settings P Keyword: "fall" or "fell" Occurrence Report: 4.2 Falls Injuries/breaks/fractures MDS-HC: Presence of fractures or other injuries Keyword: "injure" Skin problems or ulcers MDS-HC: Presence of pressure ulcer Keyword: "ulcer" or "sore" Infections MDS-HC: Urinary tract infection and use of indwelling catheter Keyword: "infection" Medication-related events Keyword: "reaction" or "overdose" Potentially inappropriate medication search Occurrence Report: 4.1 Medications Hospitalization MDS-HC: At least 1 overnight hospital stay, visit to the ER or emergent care in last 90 days Discharge: Hospitalized Keyword: "hospital" or names of hospitals in Winnipeg Nursing Home Placement Discharge: Placed in nursing home Keyword: "panel" or "nursing home" or "personal care home" Death Discharge: Deceased Keyword: "death" or "died" Screening Types: MDS-HC (Minimum
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".