Critical posthumanism: A double‐edged sword for advancing nursing knowledge in planetary health
Bibliographic record
Abstract
In this article, we aimed to evaluate the utility of critical posthumanism for nurses interested in planetary health-a growing area of study that requires a decentering of the human, and environmental justice considerations. We used Chinn and colleagues' method to describe and critically reflect on critical posthumanism, extending the theory analysis method to include a wide range of academic and video sources. We found that critical posthumanism is like a double-edged sword: It provides a lens through which to transcend human-centric approaches to healthcare but is marred by its lack of clarity and inaccessibility. We argue critical posthumanism can be adapted to enhance its potential at the intersection of nursing and planetary health. An analysis of critical posthumanism is followed by a discussion framed by five ways of knowing in nursing, highlighting real-world examples of how critical posthumanism can aid nurses in dealing with planetary health concerns. By exploring the intersections of critical posthumanism with nursing knowledge, we demonstrate how critical posthumanism can enable nurses to comprehend and tackle environmental issues intricately linked to human health.
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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 | 0.083 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.081 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.005 | 0.009 |
| 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".