Nurse managers’ self-evaluations of their management competencies and factors associated with their ability to develop staff
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.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".