Professional practice leadership roles
Bibliographic record
Abstract
Professional practice leadership (PPL) roles are those roles responsible for expert practice, providing professional leadership, facilitating ongoing professional development, and research. Despite the extensive implementation of this role, most of the available literature focuses on the implementation of the role, with few empirical studies examining the factors that contribute to PPL role effectiveness. This article will share the results of a research study regarding the role of organizational power and personal influence in creating a high-quality professional practice environment for nurses. Survey results from nurses and PPLs from 45 hospitals will be presented. Path analysis was used to test the hypothesized model and relationships between the key variables of interest. Results indicate that there is a direct and positive relationship between PPL organizational power and achievement of PPL role functions, as well as an indirect, partially mediated effect of PPL influence tactics on PPL role function. There is also a direct and positive relationship between PPL role functions and nurses' perceptions of their practice environment. The evidence generated from this study highlights the importance of organizational power and personal influence as significantly contributing to the ability of those in PPL roles to achieve desired outcomes. This information can be used by administrators, researchers, and clinicians regarding the factors that can optimize the organizational and systematic strategies for enhancing the practice environment for nursing and other health care professionals.
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.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.013 |
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".