Advanced Practice Nursing, Health Care Teams, and Perceptions of Team Effectiveness
Bibliographic record
Abstract
This article summarizes the results of an extensive review of the organizational and health care literature of advanced practice nursing (APN) roles, health care teams, and perceptions of team effectiveness. Teams have a long history in health care. Managers play an important role in mobilizing resources, guiding expectations of APN roles in teams and within organizations, and facilitating team process. Researchers have identified a number of advantages to the addition of APN roles in health care teams. The process within health care teams are dynamic and responsive to their surrounding environment. It appears that teams and perceptions of team effectiveness need to be understood in the broader context in which the teams are situated. Key team process are identified for team members to perceive their team as effective. The concepts of teamwork, perceptions of team effectiveness, and the introduction of APN roles in teams have been studied disparately. An exploration of the links between these concepts may further our understanding the health care team's perceptions of team effectiveness when APN roles are introduced. Such knowledge could contribute to the effective deployment of APN roles in health care teams and improve the delivery of health care services to patients and families.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".