Impact of dedicated nursing education days on nurse job satisfaction and retention
Bibliographic record
Abstract
Background: The recent pandemic has exacerbated the existing challenges in nursing retention due to heightened stress and burnout. Objective: This study aims to determine whether there is an association between dedicated nursing education days and nurse satisfaction and retention at ESHC. Method: Erie Shores HealthCare nurses (n=44) were consulted through email and gave input on the design and implementation of workshops for nurse consumption. Post-consultation nurses participated in 8-hour-long education days. A voluntary, anonymous survey of 113 nurses participating in various educational days at Erie Shores HealthCare in Leamington, Ontario, Canada, between December 2023 and April 2024 was conducted. Result: Around 73% of participants reported that the education workshops were useful to their practice and competency as nurses in Ontario and 71% expressed that the knowledge gained would be used within their practices. Approximately 73% would recommend education workshops to other hospitals and organizations for ongoing nurse training in Ontario. The survey result underscores the urgent need for action in addressing the effects of implementing and executing multiple dedicated nursing education days. Implication: Outcomes from the study have led to proposed education days on an ongoing basis and developing out to other professions such as physicians and allied health professionals. This learning is crucial in understanding and addressing the broader issue of the national nursing shortage and ongoing problems with work-life balance and should motivate us all to take immediate action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".