Newtonian science, complexity science and suicide—critically analysing the philosophical basis for suicide research: A discussion paper
Bibliographic record
Abstract
AIM: A critical discussion comparing Newtonian science and complexity science as the philosophical basis for suicide research and its impact on suicide knowledge development and clinical practice. DESIGN: Discussion paper. DATA SOURCES: A review of literature on suicide research and complexity science ranging from 2000 to 2022. IMPLICATIONS FOR NURSING: Suicide research based on a Newtonian worldview can have negative consequences for suicide knowledge development and can permeate nursing practice in ways that take away from addressing the complex needs of patients, their families and healthcare teams. CONCLUSION: A Newtonian worldview as a philosophical basis for research is insufficient for the study of a phenomenon as complex as suicide. A complexity science approach is better suited to the study of suicide given the multiple, interrelated, emerging factors that can contribute to a person's decision to end their own life. IMPACT: Suggestions are provided as to how a complexity science approach to the research of suicide can inform useful knowledge development that better meets the needs of individuals facing suicidality and their families. Researchers, healthcare administrators and nurses providing care to those struggling with suicidality can benefit from adopting a complexity science worldview in addressing this multifaceted phenomenon.
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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 | 0.041 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.011 |
| 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".