CHARACTERIZING AND OPTIMIZING COLLABORATIVE PRACTICES BETWEEN NURSE PRACTITIONERS (NP) AND REGISTERED NURSES IN THE MANAGEMENT OF HYPERTENSION IN NP CLINICS: STUDY PROTOCOL
Bibliographic record
Abstract
Objective: Access to primary health care services through a traditional medical model remains challenging in Canada. To address this issue, starting in 2023, nurse practitioners (NP) lead clinics staffed by NP and registered nurses (RN) have been introduced by the government of the Canadian province of Quebec. Despite the key role of NP and RN, their collaborative practice has not been studied. To characterize NP-RN collaborative practices, the context of care related to the management of hypertension (HTN) provides an opportunity. HTN is the major risk factor for cardiovascular disease and dementia and is associated with more than 10 million deaths per year worldwide. Moreover, the NP/RN team can provide care for most adults with HTN: education, prevention, screening, diagnosis, treatment, and follow-up. The aim of this study will be to characterize and optimize NP-RN collaboration in the context of a NP clinic. The specific aims are: 1-describe the collaboration and current clinical practice between the NP and the RN in the management of HTN and to identify gaps in relation to Canadian guidelines in the management of HTN, 2- co-construct, with NPs, RNs and patients, an optimized process for systematic collaborative practices in the management of HTN. Design and method: Leveraging the Canadian interprofessional health collaborative competency framework for advancing collaboration, this multiple case study will recruit 5 NP clinics (Quebec Canada). Inclusion criteria will be as follows: staffed by NP and RN, management of adults with HTN and in operation for at least 12 months. To achieve the objectives of the study, chart audits, direct observation of collaborative practice and clinical practice in HTN management, interviews with one NP and one RN per clinic, as well as patients, on collaborative practice and hypertension management will be conducted. Results: Finally, after analysis and triangulation of the collected data, NP-RN-patient co-construction of a systematic collaborative approach to the management of HTN according to Canadian guidelines will be carried out. Conclusions: In the context of a NP clinic, this study will be the first to characterize and optimize collaborative practices between NP and RN in HTN management.
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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 | 0.048 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".