Building Capacity in Long-Term Care: Supporting Homes to Provide Intravenous Therapy
Bibliographic record
Abstract
BACKGROUND: Typically, long-term care home (LTCH) residents are transferred to hospital to access intravenous (IV) therapy. The aim of this study was to pilot-test an in-home IV therapy service, and to describe outcomes and key informants' perceptions of this service. METHOD: This service was pilot-tested in four LTCH in the Hamilton-Niagara region, Ontario. Interviews were conducted with six caregivers of residents who received IV therapy and ten key informants representing LTC home staff and service partners to assess their perceptions of the service. A chart review was conducted to describe the resident population served and service implementation. RESULTS: Twelve residents received IV therapy. This service potentially avoided nine emergency department visits and reduced hospital lengths of stay for three residents whose IV therapy was initiated in hospital. There were no adverse events. The service was well received by caregivers and key informants, as it provided care in a familiar environment and was perceived to be less stressful and better quality care than when provided in hospital. CONCLUSION: IV therapy is feasible to implement in LTCHs, particularly when there are supportive resources available and clinical pathways to support decision-making. This service has the potential to increase capacity in LTCHs to provide medical care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".