Reimagining a nursing ecosystem in an uncertain world
Bibliographic record
Abstract
The discussion paper synthesises the insights shared during a keynote panel at the 26th International Philosophy of Nursing Conference, themed "Reimagining a nursing ecosystem in an uncertain world." It delves into the substantial impact uncertainty has on nursing, offering innovative strategies for reconceptualization. Through a critical examination of evidence-based practice, the tendency to homogenise nursing is discussed, prompting advocacy for a Nietzschean political framework as a form of resistance and emancipation. Drawing inspiration from Donna Haraway, a transition from individualistic to relational approaches is needed, especially amidst social and economic instability. Furthermore, the integration of caritas and Indigenous epistemologies underscore the significance of cultural sensitivity in nursing. Meanwhile, Ubuntu philosophy champions empathy and collective values, while a convergence of Eastern and Western philosophical traditions promises to enrich nursing practice. Ultimately, the panellists advocate for overcoming rigid ideologies in favour of a pluralistic approaches, ontologies, epistemologies and ethics to address contemporary challenges, and signal for a philosophical reorientation to engage uncertainty and reshape nursing roles in an ever-evolving and world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.048 |
| Scholarly communication | 0.034 | 0.032 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".