Case Study: Realizing the Value of Nurse Practitioners in Long-Term Care
Bibliographic record
Abstract
Presently only about 9% of seniors over the age of 75 live in residential care facilities but the anticipated exponential growth of the senior population will put increasing pressure on the need for supportive, continuing care services in the years ahead (CIHI 2017a). They are on average 86 years of age with a diagnosis of dementia (67%) and some cognitive and/or functional impairment (98%) (CIHI 2017b). These compromised seniors are also more likely to use hospital services than others, and in many jurisdictions occupy acute care beds for extended periods because ongoing management of their complex conditions is often not possible within long-term care (LTC) homes. In addition, unnecessary and futile care at the end of life often leads to suffering for individuals and their families (Dobbins 2016), which might otherwise be avoided with the presence of institutionally based providers such as nurse practitioners (NPs). Christian and Barker (2009) identified NPs as a valuable resource to prevent unnecessary hospitalizations which often lead to complications and devastating results for seniors. Although evidence of the positive impact of NPs working in LTC facilities dates back to the 1970s, very few organizations have created and obtained funding support for these positions in LTC settings. This case study illustrates the impact of the NP role in a New Brunswick LTC facility; demonstrating the value of a model that includes an NP collaborating with physicians, nurses and allied health professionals. As shown in this case, the presence of an NP clearly impacted a reduction in emergency room visits and hospitalizations, events that more often than not accelerate further physical, mental and functional decline particularly among the frail elderly. Christian and Barker (2009) described the significant consequences of hospitalization for the elderly patient including irreversible decline in function, delirium and exposure to iatrogenic disease. Having an NP available within LTC affords the residents ongoing monitoring that allows for preemptive and proactive care. NPs providing oversight to the collaborative management of the care of LTC residents has the potential to not only realize cost-avoidance for the healthcare system but also enhance the quality of care for residents and their families.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".