Bibliographic record
Abstract
AIMS: The aim was to assess the current literature investigating health activism within nursing practice. DESIGN: This was a scoping review of the literature utilizing the updated Levac et al.'s framework. DATA SOURCE/REVIEW METHODS: A search of the CINAHL, PubMed, Scopus and Allied Health databases was conducted for peer-reviewed, English research published between January 2000 and April 2021. RESULTS: Thirty-one articles met the criteria for inclusion in this study. The included research in nursing and health activism was heterogeneous in topic and method and primarily conducted in North America. Four themes resulted from the inductive thematic analysis: (1) Doing Health Activism, (2) Facilitators to Engaging in Health Activism, (3) Barriers to Health Activism Engagement and (4) Limited Education. Activism was not consistently defined and the term was used interchangeably with advocacy. CONCLUSION: There is a gap between nursing scope of practice, and education and skills in health activism. There is limited research regarding health activism and what constitutes as health activism. There is an opportunity to improve health activism awareness and skills within the nursing profession and undergraduate education and to produce nursing research on health activism. IMPACT: Health activism is integral to the nursing role, however, evidence suggests nurses lack confidence to engage in activism as practice. This is important for nurses across the world and in all care specialities.
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 | 0.032 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.035 | 0.030 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".