Detriments of a Self-Sacrificing Nursing Culture on Recruitment and Retention: A Qualitative Descriptive Study
Bibliographic record
Abstract
AIM: To investigate the presence and impact of self-sacrifice within the nursing profession. BACKGROUND: Evidence suggests the existence of a culture of self-sacrifice within nursing, but its potential detriments to the profession have not been explored. DESIGN: A qualitative descriptive approach was used. METHODS: Semistructured interviews were conducted with 10 practicing nurses to explore the existence and potential implications of a self-sacrificing culture within nursing. RESULTS: All participants reported self-sacrifice within the nursing profession as the result of the prevailing stereotypical image of the "ideal nurse," leading to job dissatisfaction, presenteeism, and burnout. Younger nurses reported being less willing to self-sacrifice and consequently felt unsupported by management and senior staff, resulting in job dissatisfaction and intent to leave their job. CONCLUSION: A culture of self-sacrifice within the nursing profession may lead to job dissatisfaction, presenteeism, burnout, and retention problems, especially for younger nurses. A self-sacrificing image of nursing may also deter potential recruits from exploring a career in the 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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".