Bibliographic record
Abstract
Background and aims: Advances in technology, science and treatment have precipitated significant changes to the environment where children who suffer from life threatening illnesses and injuries receive care. Despite these changes, the paediatric critical care nurse’s role of observation has remained constant. Aims: Nurses frequently denigrate the impact their expertise has on the management of critical illness and patient recovery. Evidence has shown that nursing surveillance and intervention improve patient well-being, prevent medical error and reduce length of stay. Methods: This presentation will review current literature describing the ability of the expert nurse clinician to positively impact patient outcomes through surveillance, knowledge informed judgement and intervention in the critically ill patient. Discussion will include video reflections of key stakeholders from a major Canadian paediatric hospital including patients and families. They describe how nursing expertise impacts patient care. Results: The research, interdisciplinary team, patient and families have indicated the significance of the expert nurse clinician at the patient’s bedside. The role of nursing expertise is paramount in impacting positive patient outcomes. Conclusions: The critical care paediatric patient and the environment can be extremely complicated and complex. Nurses are crucial contributors in the care of the critically ill child.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.718 | 0.446 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".