Building Nutrition into a Falls Risk Screening Program for Older Adults in Family Health Teams in North Eastern Ontario
Bibliographic record
Abstract
Approximately 30 per cent of those over the age of 65 living in the community fall at least once each year, and a similar proportion are at nutrition risk. Screening is an important component of prevention. The objective of this study was to understand how to add nutrition risk screening to a falls risk screening program in family health teams (FHTs). Interview participants (n = 31) were staff/management, regional representatives, and clients from six FHTs that had started integrating screening. Thematic analysis was conducted. Themes identified how to develop screening programs: setting up for successful screening, making it work, and following up with risk. An overarching theme recognized "it's about building relationships". Adding nutrition risk to a falls risk screening program takes effort, and is different for each FHT based on their work flow and client population. Determining how to integrate screening into the work flow and planning to address identified risk are necessary components.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".