1 ONLINE CASE STUDY Healthcare Quarterly Driving Practice Change Through Technology Adoption and Assessment: Clinical and Economic Impact of
Bibliographic record
Abstract
Practice change can be driven by numerous factors, including opportunities for cost savings, the development of procedures with improved clinical outcomes, the identi-fication of inefficient processes and the availability of new technologies offering clinical benefits. This case reports on the implementation of the Clave ® needlefree intravenous admin-istration system, its effect on clinical nursing practice (including safety), associated patient benefits and its economic impact at St. Joseph’s Health Care (SJHC) in London, Ontario. SJHC consists of five facilities with a combined total of almost 1,900 beds and 5,500 employees. This case also illustrates the benefits of evaluating the results of technology implementation and associated processes to identify opportuni-ties for clinical practice change. SETTING Needle-stick or sharps injury is a common occurrence among healthcare professionals and a significant health risk, especially for nurses and laboratory workers. Canadian Centre for Occupational Health and Safety (CCOHS) data indicate that some hospi-tals report one-third of nursing and laboratory staff suffer needle-stick injuries annually (CCOHS 2004). Whenever systems containing needles are used, disassembled or discarded, healthcare professionals risk accidental needle-stick injury. Working with intravenous (IV) equipment has been identified as an important source of needle
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".