Factors influencing fall prevention for patients with spinal cord injury from the perspectives of administrators in Canadian rehabilitation hospitals
Bibliographic record
Abstract
BACKGROUND: Fall prevention is a priority in Canadian tertiary rehabilitation hospitals. We aimed to understand the perspectives of hospital administrators on the challenges experienced when implementing fall prevention policies/procedures for patients with spinal cord injury (SCI) in tertiary rehabilitation hospitals. METHODS: Semi-structured interviews were conducted with 10 administrators employed in six Canadian tertiary rehabilitation hospitals. Guided by an interpretive description framework, interviews were analyzed using a constant comparison approach. RESULTS: Challenges with fall prevention experienced by administrators fell into the three categories: 1) fall prevention policy and procedural challenges (e.g. fall prevention policy not SCI-specific, expectation of zero falls, determining contributing factors, learning from falls, and overall effectiveness of the fall prevention policy), 2) clinician-related challenges (e.g. variable staff adherence with the organizations' fall prevention procedures, inconsistent delivery of fall prevention education, and integrating individualized fall risks to guide clinical practice), and 3) patient-related challenges (e.g. balancing risk vs independence and rehabilitation progress, responsibility for fall prevention, and non-preventable falls). CONCLUSIONS: Fall prevention policies/procedures required by the hospitals were insufficient for clinical practice in SCI rehabilitation.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".