Frailty and Home Confinement during the COVID-19 Pandemic: Results of a Pre-Post Intervention, Single Arm, Prospective and Longitudinal Pilot Study
Bibliographic record
Abstract
The COVID-19 pandemic had severe consequences for older adults. First, COVID-19 was associated with more severe medical complications and an increased mortality rate in older compared to younger adults (1). Second, home confinement, an intervention that reduces the spread of COVID-19, was associated with adverse consequences for the older community-dwelling population (2). It broke down social networks and the continuum of primary care, resulting in medication or food delivery issues, psychological fallout and increasing frailty risks (3). Frailty assessment provides insight into the degree of older community dwellers’ health status vulnerability, social isolation and adverse health event risks, and it should be assessed before interventions are proposed (3). We designed a short assessment tool known as “Evaluation SOcio-GERiatrique” (ESOGER) for Montreal’s homebound community-dwelling older adults (3). In a phone call, ESOGER briefly assessed frailty and social isolation and provided recommendations, facilitating contact with health or social care providers who initiate appropriate health and social care plans (3). This study aims to examine the longitudinal effects of ESOGER on frailty and social isolation in Montreal’s homebound community-dwelling older adults.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".