Nursing Leadership: Making a Difference in Mental Health
Bibliographic record
Abstract
Mental health -or the lack of it -is one of the most serious challenges facing the healthcare system.The scope of the problem is staggering.According to the Canadian Mental Health Association (CMHA), by age 40 about 50% of Canadians will have or have had a mental illness.Suicide accounts for 24% of all deaths among 15-to 24-year-olds and 16% among 25-to 44-year-olds -making it one of the leading causes of death from adolescence to middle age.Almost half of those who feel they have suffered from depression or anxiety have never seen a healthcare provider or received treatment (CMHA 2017).Yet, a study by the Mental Health Commission of Canada (MHCC) estimated that in 2011 $42.3 billion was spent on treatment, care and support services for those who did seek help.The Commission further estimated that these costs would exceed $2.3 trillion within 30 years (MHCC 2013).The costs to the economy are also significant.The Conference Board of Canada reported in 2012 that mental illnesses were costing the national economy about $20.7 billion annually because of the reduced number of people in the workforce.It projected that this cost is growing at a rate of approximately 1.95% every year and would rise to $29.1 billion annually by 2030 (CBC 2012).In addition, issues such as the toll of mental illness on individuals, their caregivers and communities, negative media coverage, access to care -particularly for children and youth -further compound a difficult situation.In the face of this, is there an opportunity for nurse leaders to make a difference?Are there areas in which nurses can have a unique impact?When the editorial team at the Canadian Journal of Nursing Leadership was considering questions like this, we went to five nurse leaders currently engaged in this sector.Each leader identified an area in which they see significant opportunity for nurses to provide leadership: nursing practice, continuity of care, technology and innovation,
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.011 | 0.024 |
| Insufficient payload (model declined to judge) | 0.022 | 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".