Multiple Intravenous Infusions Phase 2a: Ontario Survey.
Bibliographic record
Abstract
BACKGROUND: Research conducted in earlier phases of this study prospectively identified a number of concerns related to the safe administration of multiple intravenous (IV) infusions in Ontario hospitals. OBJECTIVE: To investigate the potential prevalence of practices or policies that may contribute to the patient safety risks identified in Phase 1b of this study. DATA SOURCES AND REVIEW METHODS: Sixty-four survey responses were analyzed from clinical units where multiple IV infusions may occur (e.g., adult intensive care units). Survey questions were organized according to the topics identified in Phase 1b as potential contributors to patient harm (e.g., labelling practices, patient transfer practices, secondary infusion policies). RESULTS: Survey results indicated suboptimal practices and policies in some clinical units, and variability in a number of infusion practices. Key areas of concern included the following: use of primary IV tubing without back check valves when administering secondary infusions, administration of secondary infusions with/as high-alert continuous IV medications, potential confusion about how IV tubing should be labelled to reflect replacement date and time, interruptions to IV therapy due to IV pump and/or tubing changes when patients are transferred between clinical units, coadministration of continuous or intermittent infusions on central venous pressure monitoring ports, variability in respondents' awareness of the infusion pump's bolus capabilities. LIMITATIONS: Due to the limited sample size, survey responses may not be representative of infusion practices across Ontario. Answers to some questions indicated that the intent of the questions might have been misunderstood. Due to a design error, 1 question about bolus administration methods was not shown to as many respondents as appropriate. CONCLUSIONS: The Ontario survey revealed variability in IV infusion practice across the province and potential opportunities to improve safety.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".