Temporal Trends (from 2008 to 2017) in Functional Limitations and Limitations in Activities of Daily Living: Findings from a Nationally Representative Sample of 5.4 Million Older Americans
Bibliographic record
Abstract
This study's objectives are as follows: (1) to identify the temporal trends in the prevalence and the odds of activities of daily living (ADL) limitations and functional limitations (FLs) among Americans aged 65 and older; (2) to explore if these trends vary by gender and age cohort; (3) to determine if generational differences in educational attainment play a role in the observed temporal trends. A secondary analysis of the American Community Survey (ACS) was conducted for ten consecutive waves of the annual cross-sectional survey (2008-2017). The respondents were community-dwelling and institutionalized adults aged 65 and older (n = 5.4 million). The question on ADLs was "Does this person have difficulty dressing or bathing?". The question on FLs was "Does this person have serious difficulty walking or climbing stairs?". There was a substantial decline over the decade in the prevalence of ADL limitations, from 12.1% to 9.6%, and FLs, from 27.3% to 23.5%. If the 2017 prevalence rates had remained at the same level as the 2008 prevalence rates, there would have been an additional 1.27 million older Americans with ADL limitations and 1.89 million with FLs. Adjusting for educational attainment substantially attenuated the odds of the decline for both ADL limitations and FLs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".