In search of scientific objectivity: Is there such a property for paediatric concussion?
Bibliographic record
Abstract
Concussions are a significant public health problem worldwide. This brain injury is problematic in the paediatric population for a variety of reasons; however, the enquiry into these problems has been mainly through the biomedical perspective. This approach has impacted nursing knowledge and practice of children and youth with a concussion, primarily since other perspectives are viewed as not being objective. In this manuscript, I draw on Thomas Kuhn's view of objectivity to evaluate the biomedical perspective of concussion. I utilize current research and clinical examples to illustrate the advantages and drawbacks of this perspective for nursing. From this discussion, I propose an alternative perspective to capture the complexity of paediatric concussions for nursing, a systems perspective. Although I argue for an alternative perspective to approach paediatric concussions for nursing, I maintain that the biomedical perspective can be incorporated as one part of nursing knowledge and practice for paediatric concussion.
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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 | 0.293 | 0.401 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.009 | 0.133 |
| Scholarly communication | 0.024 | 0.034 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.012 | 0.023 |
| 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".