The image of nursing in the media: A scoping review
Bibliographic record
Abstract
AIM: To systematically review the available evidence from research exploring the image of nurses in the media. BACKGROUND: Nurses have historically faced many challenges and have received media attention for such efforts. However, the image of nursing traditionally conveyed by media has failed to represent the real character and a positive image of the nursing profession. REVIEW METHODS: For this scoping literature review, a search was conducted in PubMed, CINAHL, Scopus, PsycINFO, Web of Science and Dialnet to identify studies written in English, Spanish or Portuguese from the earliest date in the databases until February 2022. Four authors were involved in a two-stage screening process. Data were subjected to quantitative content analysis. A decade-by-decade analysis was performed to track the evolution of the research. RESULTS: Sixty studies were included. The analysis shows (1) an increasing interest in analysing the portrayal of nurses and nursing in media over time, especially from 2000 onwards; (2) a prevailing trend of focusing on one form of media when analysing the portrayal of nurses; (3) qualitative designs as the most frequent method for exploring the image of nursing; and (4) a predominantly negative image conveyed by media. CONCLUSIONS: There is a notable body of scientific evidence about the image of nurses and nursing portrayed in media. The interest in analysing media depictions of nursing has a long history. The included studies' samples showed heterogeneity, as they were obtained from different media, periods and countries. IMPLICATIONS FOR NURSING: This scoping review is the first systematic review to provide a comprehensive map of what has been studied thus far regarding media depictions of nursing. It confirms the imperative need for nurses in different settings (academic, assistance or management fields) to maintain a proactive attitude towards addressing images of nursing and ensuring accurate representations.
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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 | 0.019 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.039 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".