A unique practice model for Nurse Practitioners in long‐term care homes
Bibliographic record
Abstract
AIM: This paper is a report of a study examining a practice model for Nurse Practitioners (NPs) working in long-term care (LTC) homes and its impact on staff confidence, preventing hospital admission, and promoting early hospital discharge. BACKGROUND: The recent introduction of NPs in LTC homes in Ontario, Canada, provided an opportunity to explore unique practice models. In a pilot project, two full-time equivalent NPs provided primary care to a consortium of 22 homes serving approximately 2900 residents. The practice model was based on the specific needs of the homes and residents. METHODS: The NPs working in this project prospectively collected data (from July 2003 until June 2004) on their clinical activities and resident outcomes. Directors of Care (n = 18) of the participating homes completed a questionnaire (March 2004) assessing the impact on prevention of hospitalization and staff confidence. FINDINGS: The NPs had 2315 clinical contacts in the 1-year period; the majority (64%) were follow-up contacts. Many contacts were for uncomplicated medical problems or more complex but straightforward medical issues, and had positive outcomes. Hospital admission was prevented in 39-43% of cases. NPs had a positive impact on improving staff confidence, but no impact on facilitating early discharge from hospital. CONCLUSION: Practice models designed to meet the distinctive needs of LTC homes and residents can enhance quality of care, even with low NP:resident ratios. Participation of key stakeholders in the identification of care priorities and planning contributed to the success of this model.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".