Bibliographic record
Abstract
AIM: An inaccurate image of the nursing profession can negatively affect staff recruitment, resource allocation, and the perception of nursing professionalism. Previous research has investigated how nurses were portrayed in the traditional media but relatively little is known from the perspective of the Internet. Therefore, the present study aimed to explore how the nursing profession is portrayed on the Internet by using two popular sources of photographic images. METHODS: The first 100 images that were obtained using the search term "nurse" on Google Images and Shutterstock were analyzed. The distribution of the image attributes between the two websites was compared with Fisher's exact test. The text description of the images that were obtained from Shutterstock also was analyzed. RESULTS: In the 171 images with at least one nurse in them, the nurses were predominately female (91%). The facial expression of the nurses was mostly smiling (85%) and 68% of the nurses had a stethoscope. For those with their hands visible in the images, 39% were holding documents, writing boards, or tablet computers and 29% were shown touching patients. Only 7% were depicted as using medical devices. CONCLUSIONS: While most of the nursing images were relatively professional-looking, the nurses were portrayed only as engaging in comforting patients and recording data. Nurses who were engaged in clinical tasks or scientific activities, such as research, were absent in the portrayals. A plan needs to be developed to accurately and comprehensively represent the nursing profession on the Internet.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".