Measuring Frailty in Older Canadians: An Analysis of the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Introduction: Frailty is characterized by vulnerability to declining health and increased risk for adverse health outcomes. Measuring frailty would be beneficial for developing interventions and assessing healthcare resource needs. No standardized measurement tool for frailty has been established. The objective of this thesis was to evaluate the frailty of participants in the Canadian Longitudinal Study on Aging (CLSA). Methods: A Frailty Index (FI) was constructed for CLSA participants based on the cumulative deficit theory of frailty. Exploratory factor analysis was conducted to study the underlying constructs of frailty and identify key factors. A hypothesized measurement model for frailty was specified. The model was modified and tested using structural equation modelling (SEM) to improve goodness-of-fit. A new frailty measurement tool was created and the construct validity of the new tool and the Frailty Index were evaluated. Results: A FI was calculated for 20,874 CLSA participants (Mean 0.14 SD 0.07). The maximum FI value was 0.68. A model containing all hypothesized variables had good fit of the data, and all variables contributed significantly. A simplified model also showed good fit and included four domains: upper-body strength, lower-body strength, dexterity, and depressive symptoms. These results persisted in an independent dataset. A Simplified Frailty (SF) score was created based on this simplified model. The FI and SF scores showed significant agreement and associations with sociodemographic variables were as predicted. Conclusions: A FI was simple to construct in the CLSA, having good fit of the data and construct validity. These results are consistent with previous research on the cumulative deficit theory of frailty. A simplified frailty model revealed key domains of frailty and resulted in a potentially useful short screening tool. The FI is recommended as a valid and reproducible approach for measuring frailty in the CLSA and similar population datasets.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".