Implementing the Supportive Supervision Intervention for Registered Nurses in a Long‐Term Care Home: A Feasibility Study
Bibliographic record
Abstract
BACKGROUND: This pilot study was conducted in response to the call in 2009 by the International Association of Gerontology and Geriatrics to focus on effective leadership structures in nursing homes and to develop leadership capacity. Few researchers have evaluated interventions aimed at enhancing the leadership ability of registered nurses in long-term care. AIM: The aim of the pilot study was to test the feasibility of a three-part supportive supervisory intervention to improve supervisory skills of registered nurses in long-term care. METHODS: A repeated measures group design was used. Quantitative data were collected from healthcare aides, licensed practical nurses (i.e., supervised staff), and registered nurses (i.e., supervisors). Focus groups with care managers and supervisors examined perceptions of the intervention. RESULTS: There were nonsignificant changes in both the registered nurse supervisors' job satisfaction and the supervised staff's perception of their supervisors' support. Supervised staff scores indicated an increase in the use of research utilization but did not reflect an increase in job satisfaction. Focus group discussions revealed that the supervisors and care managers perceived the workshop to be valuable; however, the weekly self-reflection, coaching, and mentoring components of the intervention were rare and inconsistent. CONCLUSIONS: While the primary outcomes were not influenced by the Supportive Supervision Intervention, further effort is required to understand how best to enhance the supportive supervisory skills of RNs. Examples of how to improve the possibility of a successful intervention are advanced. IMPLICATIONS: Effective supervisory skills among registered nurses are crucial for improving the quality of care in long-term care homes. Registered nurses are receptive to interventions that will enhance their roles as supervisors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".