Nurse managers’ self-evaluations of their management competencies and factors associated with their ability to develop staff
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
The purpose of this study was to clarify how Japanese nurse managers (i.e., “shunin”) or higher-ranked positions self-rate their nursing management competencies and to identify factors associated with their ability to develop staff. Data were collected using a questionnaire based on the 41-item Management Index for Nurses. This index assesses the competencies related to six components of nursing management: planning, motivating staff, developing staff, communication, organization, and ensuring safety. The total possible score is 205 points. The mean percentage score for each component was calculated based on the responses from 118 participants (107 women; mean age = 44.1 ± 7.0 years). Results showed that the mean percentage score for competencies related to ensuring safety was, by far, the highest (71.8%), and the lowest was for competencies related to organization (47.6%). Principal factors found to be associated with participants’ ability to develop staff were “gathering and using information” (a subscale of “educational background and interests”) and “supportiveness of the work environment”. These results suggest that, to improve nurse managers’ competencies related to their ability to develop staff, hospitals need to establish continuing education systems that offer nurse managers convenient educational opportunities in management science, either on-site or at a higher education institution; and develop an in-house support system that enables managers to obtain counseling when practical management concerns cause them stress.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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