Abstract WP354: Hospitalized Stroke Patient Falls Rates Reduced by 50% after Stroke Survivor Focus Group
Bibliographic record
Abstract
Stroke survivors are well known to fall both in the hospital and in the community settings. Fall prevention literature recommends the use of a variety of interventions, or fall ’bundles’, to reduce hospitalized patient falls. The organization’s stroke collaborative practice team (SCPT) began monitoring hospitalized stroke patient fall rates in 2010. Even when the hospital’s organizational rates dropped, stroke patient fall rates did not. It was noted that there were no bundle strategies specifically targeting the array of disabilities that can be caused from a stroke, nor what is it clear what they should be. Thus, a falls focus group was conducted with the hospital’s stroke survivor and caregiver support group in 2011. The purpose of the meeting was to discuss their experiences with falls, review fall prevention strategies from our organization, and see if they had any suggestions that may prevent hospitalized stroke patients from falling. Over 20 stroke survivors and caregivers attended a videotaped meeting. All stroke survivors, and several caregivers, indicated with a show of hands, that they fell at home and/or while in the hospital. A robust discussion followed where many verbalized that as a result of their deficits, such as impulsivity, memory changes or aphasia, their felt our current fall bundle would not prevent a stroke patient from falling. They felt having someone in the room was a best practice and if no one could be in the room, they recommended using bed alarms. Based on their feedback, the SCPT created a new practice so all stroke patients would be placed on a bed alarm on admission. This allows everyone time to adjust to limitations cause by any new disabilities. Inpatient rehabilitation also added low beds. Data collected January to June 2011 showed on average 2 stroke patients fell each month. The new practices were implemented over the next several months. Data from November 2011- June 2012 showed fall rates dropped to approximately 1 a month, or a 50% reduction in hospitalized stroke patient falls. The value of the patient and family experience can be a powerful tool in improving performance and outcomes. Incorporating their feedback on fall prevention strategies in our organization has helped reduce hospitalized stroke patient falls.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".