Growing Practice Specialists in Mental Health: Addressing Stigma and Recruitment with a Nursing Residency Program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the growing prevalence and healthcare needs of people living with mental illness, the stigma associated with mental health nursing continues to present challenges to recruiting new nurses to this sector. As a key recruitment strategy, five mental health hospitals and three educational institutions collaborated to develop and pilot an innovative nursing residency program. The purpose of the Mental Health Nursing Residency Program was to dispel myths associated with practising in the sector by promoting mental health as a vibrant specialty and offering a unique opportunity to gain specialized competencies. The program curriculum combines protected clinical time, collaborative learning and mentored clinical practice. Evaluation results show significant benefits to clinical practice and an improved ability to recruit and retain nurses. Nursing leadership was crucial at multiple levels for success. In this paper, we describe our journey in designing and implementing a nursing residency program for other nurse leaders interested in providing a similar program to build on our experience.
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.
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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it