Can They Keep Going on Their Own? A Four-Year Randomized Trial of Functional Assessments of Community Residents
Bibliographic record
Abstract
ABSTRACT Objectives:Are people 75 or over enabled to stay at home longer through annual assessments and referrals to health/social services than through assessments only or without assessments? Design:randomized controlled trial Participants:520 people 75 or over living in their own homes Intervention:Four annual RAI-HC computerized functional assessments. Intervention group 1: elders and primary caregivers received the results and were invited to take appropriate actions. Intervention group 2: elders and primary caregivers were offered referrals to health/social services. Measurements/Outcomes:death, institutionalization, home care services, RAI-HC scores, self-rated health, perceived self-efficacy, caregiver burden Results:By the end of the study, annual functional assessment and offers of referrals to health/social services led to a greater use of home care (6.3%) than did assessment alone (1.8%), but there were no significant differences in death rates, institutionalization, perceived self-efficacy, self-rated health status, or caregiver burden scores between groups. Conclusion:We discovered that this was a group of healthy seniors. Multi-dimensional functional assessment is time- and labour-intensive and should be targeted at the minority of least self-reliant seniors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".