Exploring the experiences of advanced practice psychiatric nurses : implications for developing a psychiatric nurse practitioner role
Bibliographic record
Abstract
The provision of mental health services in Manitoba, Canada has been affected by a number of problems, including but not limited to: prolonged wait times for services, varying availability of psychiatrists, differing attitudes towards the recovery movement, and shifting scopes of practice among professional groups. These problems appear to have created inefficiencies in existing mental health services, such as the absence of services for vulnerable populations, and ambiguity concerning the best way to plan for services. Effective problem solving involves taking an inventory of what resources already exist or are easily obtained that could increase the efficiency and effectiveness of systems. In mental health services, one such exploration is to consider what further role advanced practice psychiatric nursing (APPN) might play in the delivery of mental health services in Manitoba. The research question in this study was, “What are the experiences of clinical advanced practice psychiatric nurses?”. Nine registered psychiatric nurses (RPNs) participated in one to one interviews using van Manen’s hermeneutic phenomenology. Four main themes emerged from the data analysis: practice affected by the role and availability of other health care providers, practice from a person-centered perspective, pushing the frontiers, and navigating institutional systems and structures. Exploring the experiences of APPNs illuminated ideas which can be applied to positively affect the delivery of mental health services in Manitoba, through the possible creation of a psychiatric nurse practitioner (PNP) role.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.027 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".