The image of nursing: A glimpse of the Internet
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it