Public Opinion Leadership in Nursing Practice: A Rogerian Concept Analysis
Bibliographic record
Abstract
In the Dutch nursing context, work remains in strengthening the voice of nurses serving as frontline health care providers and board members alike. Conceptual clarity of Public Opinion Leadership (POL) in nursing practice is needed to provide attributes, antecedents and consequences for nurses and nurse leaders so they can contribute in the public debate and policy making processes. Using Rodgers' method of evolutionary concept analysis and the key words "POL," "lobbying" and "public affairs," we searched PubMed (including MEDLINE), CINAHL, PsycINFO and Cochrane Library for articles written in English, published between January 1999 and May 2020, which resulted in a final selection of seven studies. In addition, transcripts of an expert panel discussion regarding POL were analyzed. Attributes of POL are credibility, accessibility, altruism, dynamic networking and sense of systemness. Antecedents are a clinical background, authentic authority, policy and political awareness and strategic skills. The main consequences of POL entail influencing those who are involved in policy making processes, a new generation of public opinion leaders, and the raising of bottom-up political leaders. POL is a relatively new concept for nursing, with increasing interest given the need to ensure quality of care by increasing the use of evidence in clinical practice. POL in nursing practice is defined as the action of influencing public debate regarding policy making processes by maintaining dynamic (social) networks, having a high sense of systemness, and being (clinically) credible, altruistic and accessible to peers and a wide variety of stakeholders.
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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 | 0.040 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".