Addressing Gaps in Mental Health and Addictions Nursing Leadership: An Innovative Professional Development Initiative
Bibliographic record
Abstract
Mental health and addictions services are integral to Canada's healthcare system, and yet it is difficult to recruit experienced nurse leaders with advanced practice, management or clinical informatics expertise in this field. Master's-level graduates, aspiring to be mental health nurse leaders, often lack the confidence and experience required to lead quality improvement, advancements in clinical care, service design and technology innovations for improved patient care. This paper describes an initiative that develops nursing leaders through a unique scholarship, internship and mentorship model, which aims to foster confidence, critical thinking and leadership competency development in the mental health and addictions context. The "Mutual Benefits Model" framework was applied in the design and evaluation of the initiative. It outlines how mentee, mentor and organizational needs can drive strategic planning of resource investment, mentorship networks and relevant leadership competency-based learning plans to optimize outcomes. Five-year individual and organizational outcomes are described.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".