Bibliographic record
Abstract
As health care administrators, policymakers, nursing organizations, and nurses begin to deal with the reality of a looming (and to an increasing extent, existing) serious shortage of nurses both in Canada and globally, recruitment and retention issues are again in the news. Much attention has been directed towards two responses: (a) attracting young people into the profession and helping them integrate into and identify with nursing as a lifelong career, and (b) developing sustainable retention strategies to ensure that nurses remain in nursing. One population that requires particular attention is the mid-career group of nurses. Those nurses, in their late 30s and 40s with 15 and more years of experience, have the professional memory that employers count on, the expertise that patients and clients require, and the experience and wisdom that young nurses depend on for coaching, mentoring, and support. Retention strategies targeted to these mid-career nurses require a diverse set of activities that are focused on those nurses' specific stage of personal and professional development and that recognize their unique needs. The purpose of this article is to describe a program that targets mid-career nurses, predominantly women who, having spent much of their careers and lives caring for others-children, parents, patients-are beginning to question their own futures. The goals of the program and the experiences of one group of nurses in the program, as well as the results of a two-year follow up with them, will be discussed. Recommendations for future retention strategies will also be offered.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".