Measuring Leadership Practices of Nurses Using the Leadership Practices Inventory
Bibliographic record
Abstract
BACKGROUND: Originally developed for educational use, the Leadership Practice Inventory (LPI) is used to measure leadership practices in nursing research. There is limited reporting of LPI psychometric properties when used to measure leadership practices of nurses. OBJECTIVE: This study aimed to investigate psychometric properties of the LPI when used to measure the leadership practices of nurses. METHOD: Data from 67 LPI-self and 347 LPI-observer respondents were used to establish LPI psychometric properties. Dimensionality of the LPI was investigated using exploratory principal components analysis, and LPI construct validity was established by exploring correlations with theoretically related concepts and a known-groups approach. The predictive validity of the LPI was investigated using regression analysis to determine whether observer-reported leadership practices of established and aspiring nurse leaders predict observer ratings of the effectiveness of the organization environment. Reliabilities of the new factor solution were explored. RESULTS: Factor analysis found that the identified three-factor solution has psychometric properties at least as strong as those found with the original five-factor LPI solution. DISCUSSION: The three-factor solution is advocated for use in nursing research because of the strong psychometric properties, lighter respondent burden, and decrease in research costs, as compared with the traditional five-factor solution. When used as an educational tool, the five-factor LPI may be preferred because it may be more useful for examining a greater number of leadership behaviors.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".