Remembering Nightingale in her turbulent times: Introducing the Theory of Integral Nursing in our turbulent times
Bibliographic record
Abstract
AIM: To connect selected examples from Florence Nightingale's life with concerns for the distress nurses are experiencing worldwide. BACKGROUND: We live in turbulent times of disease, armed conflict, vast climate change, and social injustice-factors exacerbating illness and impacting health-factors Nightingale also addressed throughout her life. SOURCES OF EVIDENCE: The Theory of Integral Nursing (TIN) is introduced with related examples of nursing's problems and solutions seen through integral lenses at the individual, group, grassroots, and global levels. Illustrating these points, relevant Nightingale quotes and life narratives illustrate how she responded, in her time, to the challenges we face in our time. DISCUSSION: Leading reflections focus on changes needed within nursing's culture to encourage proactive innovations and public advocacy for the challenges nurses face-for nurses to be seen and heard. CONCLUSION: Nurses are a potentially significant force to bring our trusted caring and compassion for suffering into advocacy for a better world. IMPLICATIONS FOR NURSING PRACTICE: Nightingale's vision for maintaining excellence in 'sick-nursing' practice is described with a corresponding aim to develop 'health-nursing' practices to address the causes of illness and injury through public advocacy. IMPLICATIONS FOR NURSING AND HEALTH POLICY: Recommendations connect Nightingale's hope for future policy leadership with recent global policy calls to sustain positive change within nursing's culture and the global healthcare workforce.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".