An Innovative and Comprehensive Approach to Nursing Workforce Sustainability in Nova Scotia
Bibliographic record
Abstract
The COVID-19 pandemic has revealed long-standing deficiencies with existing nurse recruitment and retention approaches, resulting in critical shortages of nursing capacity that are set to worsen without appropriate action. Decades of evidence and experience suggest that a multi-pronged approach that fosters an enabling and supportive work environment for nurses across all stages of their working lifespan will be required to build a more sustainable nursing workforce. This paper demonstrates Nova Scotia's innovation in creating a comprehensive, evidence-informed approach to nursing workforce planning and management, including key strategic areas of action related to (1) facilitating entry into the workforce, (2) investing in the active workforce and (3) enhancing support for and managing attrition of the workforce. This paper also offers nursing leaders a series of reflections on current learnings in the implementation of this innovative and person-centred approach to nursing workforce sustainability. Recognizing the pressing need for action, Nova Scotia Health and provincial leaders have and are implementing strategic innovations to enhance the nursing workforce. These include: (1) investment in organizational capacity for evidence-based innovation, (2) development of collaborative relationships between both internal stakeholders and community partners and (3) creation of mechanisms for meaningful engagement and co-design of locally relevant innovative solutions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".