Mobile computing and the quality of home care nursing practice
Bibliographic record
Abstract
We investigated the effects of the introduction of mobile computing on the quality of home care nursing practice in Québec. The software, which structured and organized the nursing activities in patients' homes, was installed sequentially in nine community health centres. The completeness of the nursing notes was compared in 77 paper records (pre-implementation) and 73 electronic records (post-implementation). Overall, the introduction of the software was associated with an improvement in the completeness of the nursing notes. All 137 nurse users were asked to complete a structured questionnaire. A total of 101 completed questionnaires were returned (74% response rate). Overall, the nurses reported a very high level of satisfaction with the quality of clinical information collected. A total of 57 semi-structured interviews were conducted and most nurses believed that the new software represented a user-friendly tool with a clear and understandable structure. A postal questionnaire was sent to approximately 1240 patients. A total of 223 patients returned the questionnaire (approximately 18% response rate). Overall, patients felt that the use of mobile computing during home visits allowed nurses to manage their health condition better and, hence, provide superior care services. The use of mobile computing had positive and significant effects on the quality of care provided by home nurses.
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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 | 0.003 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".