Experiencia de implantación de la Guía de Buenas Prácticas de RNAO «Prevención de caídas y lesiones derivadas de las caídas» en Atención Primaria de Tenerife
Bibliographic record
Abstract
Falls are a public health problem with worldwide relevance and are considered the second cause of death due to involuntary trauma. Nurses have a central role in preventing falls by implementing evidence-based recommendations. Good practice guidelines (GBP) allow care to be provided based on the best and most current evidence. The Nurses Association of Ontario (RNAO) develops GBP that it expands worldwide thanks to the Program of Centers Committed to Excellence in Care, BPSO®, in which the Primary Care Management of Tenerife participates, as a candidate center, implementing the Guide «Preventing falls and reducing injury resulting from falls». The objectives of this study were to describe the profile of the patient included in «Home Health Care Service» in the ZBS of the Tenerife Health Area where the GBP is implemented.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".