The imageof thenurseon the Internet
Bibliographic record
Abstract
The media image of the nurse is a source of concern because of its impact on: recruitment into the profes-sion; the decisions of policy makers who enact legis-lation that defines the scope and financing of nursing services; the use of nursing services by consumers; and the self-image of the nurse. This article reports on the results of a study of the image of nursing on the Internet utilizing content analysis methodology. A total of 144 Websites were content-analyzed in 2001 and 152 in 2004. Approximately 70 % of the Internet sites showed nurses as intelligent and educated and 60% as respected, accountable, committed, competent, and trustworthy. Nurses were also shown as having special-ized knowledge and skills in 70 % (2001) and 62 % (2004) of the Websites. Scientific/research-oriented, compe-tent, sexually promiscuous, powerful, and creative/ innovative increased from 2001–2004 while commit-ted, attractive/well groomed, and authoritative images decreased. Doctoral-prepared nurses were evident in 19 % of the Websites in 2001 and doubled in 2004. The results of this study suggest that there are important opportunities to use the Internet to improve the image of the nurse. The image of the nurse is noted as a significantproblem in many countries of the world includingAustralia,1,2 Britain,3,4 Canada,5,6 Ireland,7 Poland,8 Hong Kong,9 Taiwan,10 and the United States.11–12 One of the major influences on the image of the nurse is the mass media portrayal of the profession.18–20 What individuals see, hear, and read in the media influence the image they develop of nursing. Although there have been a few successful efforts to reshape the media image of nursing, the image is still largely inaccurate and negative.2,11,21–25 Nurses are under-represented and often invisible in media portrayals of healthcare. This article reports the results of a study investigat-ing the newest form of the mass media, the Internet. The Internet image of nursing has become increasingly more important in recent years because of the public’s (especially young adults ’ and teens’) growing use of this form of media to obtain information and learn about the world.26
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".