Creating a Brand Image for Public Health Nursing
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
Public health nurses (PHNs) have declined as a proportion of both the nursing and the public health workforces in the past 2 decades. This decline comes as 30 states report public health nursing as the sector most affected in the overall public health shortage. Taken together, these data point to a need for renewed recruitment efforts. However, the current public images of nurses are primarily those of professionals employed in hospital settings. Therefore, this paper describes the development of a marketable image aimed at increasing the visibility and public awareness of PHNs and their work. Such a brand image was seen as a precursor to increasing applications for PHN positions. A multimethod qualitative sequential approach guided the branding endeavor. From the thoughts of public health nursing students, faculty, and practitioners came artists' renditions of four award-winning posters. These posters portray public health nursing-incorporating its image, location of practice, and levels of protection afforded the community. Since their initial unveiling, these posters have been distributed by request throughout the United States and Canada. The overwhelming response serves to underline the previous void of current professional images of public health nursing and the need for brand images to aid with recruitment.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 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