Aligning Career Development with Organizational Goals: Working towards the Development of a Strong and Sustainable Workforce
Bibliographic record
Abstract
The rapidly changing world of healthcare is faced with many challenges, not the least of which is a diminishing workforce. Healthcare organizations must develop multiple strategies, not only to attract and retain employees, but also to ensure that workers are prepared for continuous change in the workplace, are working at their full scope of practice and are committed to, and accountable for, the provision of high-quality care. There is evidence that by creating a healthier workplace, improved patient care will follow. Aligning Healthy Workplace Initiatives with an organization's strategic goals, corporate culture and vision reinforces their importance within the organization. In this paper, we describe an innovative pilot to assess a career development program, one of multiple Healthy Workplace Initiatives taking place at Providence Care in Kingston, Ontario in support of our three strategic goals. The results of the pilot were very encouraging; subsequent success in obtaining funding from HealthForceOntario has allowed the implementation of a sustainable program of career development within the organization. More work is required to evaluate its long-term effectiveness.
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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 | 0.013 | 0.013 |
| 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 | 0.006 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".