The impact of fall risk assessment on nurse fears, patient falls, and functional ability in long-term care
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine whether providing fall risk information to long-term care (LTC) nurses affects restraint use, activities of daily living (ADL), falls, and nurse fears about patient falls. METHODS: One-hundred and fifty LTC residents were randomized to a fall risk assessment intervention or care-as-usual group. Hypotheses were tested using analyses of variance and path analyses. RESULTS: Restraint use was associated with lower ADL scores. In the intervention group, there ceased to be significant relationships between nurse fears about falls and patient falls (after controlling for actual patient risk; post-intervention, nurse fears about falls were based on realistic appraisals), and between fears and restraints (i.e. unjustified nurse fears became less likely to lead to unjustified restraint use). No group differences in falls were identified. CONCLUSION: Despite a lack of group differences in falls, results show initial promise in potentially impacting resident care. Increasing intervention intensity may lead to fall reductions in future research. IMPLICATIONS FOR REHABILITATION: Given the high prevalence rates of falls in LTC and associated injuries, prevention programs are important. Nurse fears about patient falls may impact upon restraint use which, when excessive, can interfere with the patient's ability to perform ADL. Excessive restraint use, due to unjustified nurse fears, could also lead to falls. Providing accurate, concise information to nursing staff about patient fall risk may aid in reducing the association between unjustified nurse fears and the resulting restraint use that can have potential negative consequences.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".