Nursing services: an imperative to health care marketing
Bibliographic record
Abstract
Nurses comprise a significant portion of the work force in any health care organization. This work force is one having the maximum exposure to patients. Further, they work hand in hand with the various departments of the organization. Though an unintended consequence of nursing care, nurses form a potential marketing tool for any health care organization. In that marketing tool role, however, there are hindrances that nurses face. These may result in an unhappy work environment and potentially impact the overall image of the organization. Added to this, nurses may not always be equipped with the knowledge and expertise they need to meet current demands of their position and thus not promote the best nursing role for marketing purposes. Interestingly, good nursing care goes hand-in-hand with good marketing efforts in spite of this being an unintended consequence. The promotion of a strong and highly capable nursing image is an important strategy in marketing of health care services. The evolution of professional organizations and accreditation agencies has resulted in setting specific standards of practice for nursing graduates. These standards help to ensure delivery of patient care of some predetermined quality. Indirectly this offers marketability to the organization by promotion of the nursing image. At the executive level, nurse leaders can play an important role in development of nursing strategy formulation and at the same time influence strategic marketing design. This paper provides an overview of the role nurses may play in certain aspects of marketing.
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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 | 0.030 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".