Delivery of High-Tech Home Care by Hospital-Based Nursing Units in Quebec: Clinical and Technical Challenges
Bibliographic record
Abstract
BACKGROUND: The role that hospital-based nurses should play in the delivery of high-tech home care, and how they should be supported in that role, are topics that remain understudied. Our research objective was to document how hospital-based nursing teams perceive and deal with the clinical and technical challenges associated with the provision of high-tech home care. METHODS: Four home care interventions were selected: antibiotic intravenous therapy, parenteral nutrition, peritoneal dialysis and oxygen therapy. A self-administered survey was sent to all hospital-based units providing these interventions in the province of Quebec, Canada (n = 154; response rate: 70.8%). We used descriptive statistical analyses to derive mean values for scores on either a five- or a six-level Likert scale. RESULTS: Despite variation across the four interventions, our results indicate that while nursing teams believe these interventions increase patients' autonomy, they also recognize that they generate anxiety and impose constraints on patients' lives. Nurses must increase efforts to deal with both clinical and technical challenges and help patients overcome the barriers to appropriate use of home care technologies. CONCLUSIONS: While nursing teams generally perceive high-tech home care as beneficial, they still experience significant technical and clinical challenges. Some of these challenges could be addressed by strengthening professional training initiatives, while others require broader home care policy interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".