A mixed methods quality improvement study to implement nurse practitioner roles and improve care for residents in long-term care facilities
Bibliographic record
Abstract
Abstract Background To better meet long-term care (LTC) residents’ (patients in LTC) needs, nurse practitioners (NPs) were proposed as part of a quality improvement initiative. No research has been conducted in LTC in Québec Canada, where NP roles are new. We collected provider interviews, field notes and resident outcomes to identify how NPs in LTC influence care quality and inform the wider implementation of these roles in Québec. This paper reports on resident outcomes and field notes. Methods Research Design:This mixed methods quality improvement study included a prospective cohort study in six LTC facilities in Québec.Participants:Data were collected from September 2015–August 2016. The cohort consisted of all residents (n = 538) followed by the nurse practitioners. Nurse practitioner interventions (n = 3798) related to medications, polypharmacy, falls, restraint use, transfers to acute care and pressure ulcers were monitored.Analysis:Bivariate analyses and survival analysis of occurrence of events over time were conducted. Content analysis was used for the qualitative data. Results Nurse practitioners (n = 6) worked half-time in LTC with an average caseload ranging from 42 to 80 residents. Sites developed either a shared care or a consultative model. The average age of residents was 82, and two thirds were women. The most common diagnosis on admission was dementia (62%,n = 331). The number of interventions/resident (range: 2.2–16.3) depended on the care model. The average number of medications/resident decreased by 12% overall or 10% for each 30-day period over 12 months. The incidence of polypharmacy, falls, restraint use, and transfers to acute care decreased, and very few pressure ulcers were identified. Conclusions The implementation of NPs in LTC in Québec can improve care quality for residents. Results show that the average number of medications per day per resident, the incidence of polypharmacy, falls, restraint use, and transfers to acute care all decreased during the study, suggesting that a wider implementation of NP roles in LTC is a useful strategy to improve resident care. Although additional studies are needed, the implementation of a consultative model should be favoured as our project provides preliminary evidence of the contributions of these new roles in LTC in Québec.
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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 | 0.040 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".