Improving falls risk screening and prevention using an e-learning approach
Bibliographic record
Abstract
AIM: This study investigated the impact of an e-learning education programme for nurses on falls risk screening, falls prevention and post-falls management. BACKGROUND: Falls injury within older inpatients is a major patient safety concern. METHOD: Using a pre-post design, observation of the patient and environment and patient health care record audits, were conducted following the introduction of a falls e-learning education programme. RESULTS: Audits of patient health care records (using the Falls Chart Audit Tool), together with observation of practice for 119 (pre) and 99 (post) patients, were undertaken. Initial risk screening was conducted using the Modified Ontario Stratify Scale for most patients (95%). Interventions such as a falls risk flag in the records/on beds, supervision when the patient is mobilising or in the bathroom, area clear of hazards, use of chair/bed alarms, and referral to allied health staff were significantly improved. CONCLUSIONS: Initial risk screening of patients and improvements in preventive interventions were demonstrated. IMPLICATIONS FOR NURSING MANAGEMENT: This falls e-learning programme represents a cost-effective method of increasing falls mitigation strategies within large organisations. The Falls Chart Audit Tool provides a valuable monitoring tool for managers. Falls risk screening when the patient's condition changes, requires vigilance by managers or reminders within clinical information systems.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".