The development of a wellness resource for new graduate nurses in acute care settings
Bibliographic record
Abstract
Background: The psychological well-being (PWB) of registered nurses is integral to the local working environment and healthcare systems overall. Often, new graduates experience significant psychological distress transitioning into the workforce, leading to high rates of new nurses leaving the profession. Purpose: To develop a resource to promote and protect the psychological health of new graduate nurses within Eastern Health. Methods: I performed a literature review, consultation, and environmental scan. I conducted nine informal consultations with four new graduate nurses, four registered nurses, and a nurse educator to gather local, contextualized data. Through the environmental scan, I explored how other jurisdictions (i.e., other areas in Newfoundland and Labrador and other provinces within Canada) utilize resources and/or other strategies to enhance healthcare employees' psychological health. Results: Enhancing the PWB of nurses can improve patient safety, job satisfaction, and nurse retention. I also identified several coping strategies, such as mindfulness, that significantly increase the PWB of new graduate nurses. Based on these educational strategies, I created a wellness resource comprising a presentation, lanyard tag, and brochure. Collectively, in these strategies, I discuss psychological health, signs of psychological distress, available resources within Eastern Health, and how to access these resources. Conclusion: The nursing profession is stressful, and it is vital to promote the PWB of registered nurses. I plan to implement the wellness resource during Eastern Health’s employee orientation and nursing education workshops. I will approach the Acute Care Inpatient Policy Consultant for Mental Health and Addictions about the availability of lanyard tags and brochures. Once implemented, the next step will be to plan and evaluate the effectiveness and usability of this resource for registered nurses.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".