Threads of Care—Weaving the Historical Identity of Nurses: A Discussion Paper
Bibliographic record
Abstract
• The evolution of nurses' identity has historically shaped the profession's image. • This paper categorizes 4 key identity profiles that define nursing's past and present. • Insights can help educators empower nurses to embrace and advocate their professional roles. To critically analyze nurses' professional identity by exploring identity profiles that have shaped the nursing profession over time. Discussion paper grounded in theoretical and conceptual analysis. Four key identity profiles were identified throughout the history of nursing: (i) Primitive Image of the Nurse : characterized by a diffuse, limited identity; (ii) Religious Image : embodying a charitable, servile, and legitimizing role; (iii) Deconstruction of a Declining Identity and Construction of an Identity of Resistance : marking a shift towards self-definition and professional advocacy; and (iv) Professional Identity of the Nurse : emerging as a project-based, autonomous identity emphasizing skill and professional legitimacy. The construction of nurses' professional identity has been marked by both challenges and evolution, especially in achieving societal and peer recognition. Educators play a crucial role in guiding future nurses through this historical journey to instill a strong professional identity. By understanding this trajectory, new nurses are empowered to advocate for their role and contribute to the ongoing development of the nursing profession.
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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 | 0.025 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.030 | 0.030 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".