Challenges, Extent of Involvement, and the Impact of Nurses’ Involvement in Politics and Policy Making in in Last Two Decades: An Integrative Review
Bibliographic record
Abstract
PURPOSE: To determine nurses' challenges, extent of involvement, and the impact of involvement in politics and policy making. ORGANIZING CONSTRUCT: Nurses in politics and health policy making. METHODS: Literature was searched in PubMed, Scopus, Google Scholar, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), OVID, and Open Grey using phrases comprising the following key words: "nurses", "policy making", "politics", "health policy", "nurses involvement in policy making/politics/health policy", "nurses challenges in policy making/politics/policy", and "impact of nursing policy making/politics/health policy"; 22 articles published from January 2000 to May 2019 were included. FINDINGS: The major challenges included intra- and interprofessional power dynamics, marginalization of nurses in policy making, and nursing profession-specific challenges. The extent of involvement was inadequate, and nurses mainly worked as policy implementers rather than as policy developers. Those nurses who participated in policy development focused on health promotion to build healthy communities and to empower nurses and the nursing profession. CONCLUSIONS: Nurses' involvement in policy making has not improved over time. Nursing institutions and regulatory bodies should prepare and encourage nurses to work as policymakers rather than implementers and advocate for the rightful place of nurses at policy-making forums. CLINICAL RELEVANCE: Preparation for health system policy making starts in the clinical settings. Educational institutions and nurse leaders should adequately prepare nurses for policy making, and nurses should participate in policy making at the organization, system, and national levels.
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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".