Teorizando sobre sistemas: uma tarefa ecológica para as pesquisas na área de segurança do paciente
Bibliographic record
Abstract
As a global safety movement gathers momentum, experts call for a "systems approach" to improve the safety of today's health care environments. Yet, what kinds of systems theories should guide the field of patient safety research? In this paper, it is argued that nurses and other health professionals can use theory and principles from the field of ecological restoration, which is the repair of damaged ecosystems, to study and strengthen the safety of health care environments around the world. When we use restoration science to theorize about health care systems, we develop the ability to think ecologically about our relations with each other and with the environments we share. As we integrate knowledge of restoration science with nurses' knowledge and other knowledge in health care, we may actually create safer health care systems for all at a human and material cost that we are able and willing to pay.
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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 | 0.024 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".