Alberta: Evaluation of Nursing Retention and Recruitment Programs
Bibliographic record
Abstract
Retention and recruitment strategies are essential to address nursing workforce supply and ensure the viability of healthcare delivery in Canada. Knowledge transfer between experienced nurses and those new to the profession is also a focus for concern. The Multi-Employer/United Nurses of Alberta Joint Committee attempted to address these issues by introducing a number of retention and recruitment (R&R) initiatives for nurses in Alberta: in total, seven different programs that were introduced to some 24,000 nurses and employers across the province of Alberta in 2001 (the Transitional Graduate Nurse Recruitment Program) and 2007 (the remaining six R&R programs). Approximately 1,600 nurses participated in the seven programs between 2001 and 2009. Of the seven strategies, one supported entry into the workplace, two were pre-retirement strategies and four involved flexible work options. This project entailed a retrospective evaluation of the seven programs and differed from the other Research to Action (RTA) projects because it was solely concerned with evaluation of pre-existing initiatives. All seven programs were launched without a formal evaluation component, and the tracking of local uptake varied throughout the province. The union and various employers faced challenges in implementing these strategies in a timely fashion, as most were designed at the bargaining table during negotiations. As a result, systems, policy and procedural changes had to be developed to support their implementation after they became available.Participants in the programs indicated improvements over time in several areas, including higher levels of satisfaction with work–life balance, hours worked and their current practice and profession. The evaluation found that participation led to perceived improvements in nurses' confidence, greater control over their work environment, decreased stress levels, increased energy and morale and perceived improved ability to provide high-quality care. However, no formal implementation plan had been developed or made available to assist employers with implementation of the programs. The findings highlight the need for more discipline in communicating, implementing and evaluating initiatives such as those evaluated retrospectively in this project. In particular, key performance indicators, baseline data, monitoring mechanisms and an evaluation plan need to be developed prior to implementation.
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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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".