Evaluating Nursing Administration Instruments
Bibliographic record
Abstract
OBJECTIVE: To identify and evaluate available measures that can be used to examine the effects of management innovations in five important areas: autonomy, conflict, job satisfaction, leadership, and organizational climate. BACKGROUND: Management interventions target the context in which care is delivered and through which evidence for practice diffuses. These innovations need to be evaluated for their effects on desired outcomes. However, busy nurses may not have the time to locate, evaluate, and select instruments to measure expected nursing administration outcomes without research-based guidance. Multiple and complex important contextual variables need psychometrically sound and easy-to-use measurement instruments identified for use in both practice and research. METHOD: An expert focus group consensus methodology was used in this evaluation research to review available instruments in the five areas and evaluate which of these instruments are psychometrically sound and easy to use in the practice setting. RESULTS: The result is a portfolio of measures, clustered by concept and displayed on a spreadsheet. Retrieval information is provided. The portfolio includes the expert consensus judgment as well as useful descriptive information. CONCLUSIONS: The research reported here identifies psychometrically sound and easy-to-use instruments for measuring five key variables to be included in a portfolio. The results of this study can be used as a beginning for saving time in instrument selection and as an aid for determining the best instrument for measuring outcomes from a clinical or management intervention.
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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 | 0.130 | 0.345 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".