Implementation of the Nurse Practitioner as Most Responsible Provider model of care in a Specialised Mental Health setting in Canada
Bibliographic record
Abstract
Globally, mental health systems have failed to adequately respond to the growing demands of mental health services resulting in a disparity between the need and provision of treatment. Paucity of mental health care providers contributes to the aforementioned disparity. This can be addressed by engaging Nurse Practitioners (NPs) in an integrated model within healthcare teams. This paper describes the implementation of NPs as Most Responsible Provider (MRP) care of model in a specialised mental health hospital in Ontario, Canada. Guided by the participatory, evidence-based, patient-focused process for advanced practise nursing (APN) role development, implementation, and evaluation (PEPPA) framework, authors developed a model of care and implemented the first seven steps of the PEPPA framework - (a) define the population and describe the current model of care, (b) identify stakeholders, (c) determine the need for a new model of care (d) identify priority areas and goals of improvement, (e) define the new model of care, and (f) plan and implement the NP as MRP model of care. Within these steps, different strategies were implemented: (a) revising policies and procedures (b) harmonising reporting structures, (c) developing and implementing a collaborative practise structure for NPs, (d) standardised and transparent compensation (e) performance standards and monitoring (f) Self-Assessment Competency frameworks, education, and development opportunities. This paper contributes to the state of the knowledge by implementing NPs as MRP model of care in a specialised mental health care setting in Ontario, Canada; and advocates the need for incorporating mental health programmes within the Ontario nursing curriculum.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".