Fall-Related Injury in Older Adult Home Care Recipients: A Descriptive Population Study
Bibliographic record
Abstract
Abstract Canada is experiencing a growing aging population leading to an increase in the number of individuals receiving home care. More needs to be known about home care clients who experience fall-related injuries. The purpose of this study was to describe the characteristics of Ontario home care recipients (65 and older) who experienced fall-related injury, and the characteristics of those injuries. We conducted a population-based descriptive study using secondary data from the IC/ES data repository for the period of 2010-2014. Person-level characteristics were extracted from the Resident Assessment Instrument - Home Care and injury characteristics from ICD-10 CA codes for falls (W00-W19) in combination with injuries (S00-S99 or T00-T14), available from the NACRS database. Descriptive statistics and rates were calculated using R. Results show the population (N= 88,731) was primarily female (67.0%), the largest age group was 85-89 years old (25.5%) and hypertension was the most prevalent (83.0%) chronic condition. Clinical Assessment Protocols (CAPs) indicated need for support in management of IADLs (75.4%), falls (72.3%) and pain (70.3%). Most patients (55.8%) used nine or more medications. In 90 days prior to home care assessment, 39.6% experienced no falls, 32.4% fell once, and 26.1% fell two or more times. Injuries primarily took place within the home (38.2%). Factures were the predominant injury type (40.8%), followed by superficial injuries (19.7%). These findings create a foundation for fall-related injury prevention in home care and further research on risk identification, the efficacy of CAPs, and home environment adjustments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".