Implementation and evaluation of a mental health nursing residency program
Bibliographic record
Abstract
Objective: The Niagara Health System opened a new healthcare facility in 2013 with expansion of the Mental Health and Addictions Program. As part of operational readiness planning, the decision was made to implement a 12-week residency program to support a cohort of ten new nursing staff in attaining the knowledge, confidence, and recovery attitudes required for practice. This paper will detail the planning and implementation of a Mental Health Nursing Residency Program at the Niagara Health System. The outcomes of the residency program will be discussed as well as the challenges that will inform recommendations for future programming. There is a paucity of literature related to mental health specific residency programs, and this paper will add to the work that has already been done in this specialty area of practice. Methods: Ten nurses new to mental health participated in the 12-week residency program. The program was evaluated using pre and post measures of knowledge, confidence, and recovery attitudes at the beginning and end of the residency program. Retention rates were also examined for the cohort of new staff. Results were obtained from eight of the ten participants for data analysis. Descriptive statistics were used to describe the sample, recovery attitudes, and retention. Knowledge and confidence data were analyzed using paired t -tests. Results: Statistically significant improvements occurred in knowledge and confidence. Recovery attitudes also improved but retention of staff who participated in the residency program was not enhanced. Conclusions: A residency program can be an effective strategy to ensure that nurses new to the field of mental health and addictions have the requisite competencies for practice.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".