Creating a Brand Image for Public Health Nursing
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.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".