Image: changing how women nurses think about themselves. Literature review
Bibliographic record
Abstract
AIM: This paper presents a review of the public and professional images of nursing in the literature and explores nurse image in the context of Strasen's self-image model. BACKGROUND: Nurses have struggled since the 1800s with the problem of image. What is known about nurses' image is from the perspective of others: the media, public or other healthcare professionals. Some hints of how nurses see themselves can be found in the literature that suggests how this image could be improved. METHOD: A literature review for all dates up to 2006 was undertaken using PubMed, Medline and CINAHL databases. Additional references were identified from this literature. Sentinel articles and books were manually searched to identify key concepts. Search words used were nurse, nursing, image and self-image. The findings were examined using the framework of Strasen's self-image model. FINDINGS: Public image appears to be intimately intertwined with nurse image. This creates the boundaries that confine and construct the image of nursing. As a profession, nurses do not have a very positive self-image nor do they think highly of themselves. CONCLUSION: Individually, each nurse has the power to shape the image of nursing. However, nurses must also work together to change the systems that perpetuate negative stereotypes of nurses' image.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".