Attending Nurse Practitioners in Long-Term Care Homes Evaluation
Bibliographic record
Abstract
IntroductionIn 2014, the Ontario Ministry of Health and Long-Term Care (MOHLTC) announced funding for 75 nurse practitioners (NPs) over three years in long-term care (LTC) homes. This evaluation was approved by ICES’ Applied Health Research Question (AHRQ) team, a portfolio which answers questions from stakeholders having impact on healthcare policy. Objectives and ApproachThe purpose of this project is to evaluate the impact of the first thirty NPs hired. Changes will be evaluated using key outcome measures of resident care (e.g., early hospital discharge, emergency room bed days) identified through a literature review conducted by the MOHLTC. LTC home residents were identified using all individuals with claims in OHIP during the 2016-17 fiscal year with a location of a LTC home. LTC homes with a hired NP were considered to be cases and all other LTC homes were considered to be controls. ResultsFor part one of this evaluation, case and control LTC homes were stratified by bed size, Case Mix Index, rurality and Local Health Integration Network. Hospitalization records and emergency visits (from Discharge Abstract Database and National Ambulatory Care Reporting System) were determined for LTCH residents 6 months before and after the NP hire date of October 1, 2016. Overall, the rate of hospital admissions (per 100 residents) increased by 3.44% (8.51% to 11.94%) following the NP hire date; whereas, the rate of hospital admissions increased by 2.29% (6.55% to 8.83%) among controls. Following the NP hire date, the rate of emergency department visits also increased by 3.15% among cases (16.62% to 19.77%) in comparison to a 2.31% increase among controls (12.55% to 14.86%). Conclusion/ImplicationsThe findings from this evaluation will inform further implementation strategies of the NP program and guide decision-making of future funding opportunities. In summary, the results will inform policies to strengthen care of LTC homes and improve the quality of care of residents.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".