Determining Type and Quantity of Household Services Required for Persons With Disabilities: Using Time Use Survey Data
Bibliographic record
Abstract
One of the most important and perhaps costly categories in a life care plan is Home Care Services. This may include personal attendant care, facility care, and/or household services for persons with disabilities. This article focuses upon household services which, for the purpose of this article, refer to all of the services required to maintain a home such as yard work, home repairs, home renovations, and housecleaning. In developing a life care plan, life care planners are faced with the challenge of determining: I. Which activities should be considered for inclusion in the life care plan? 1. II. What amount of hired services should be allocated for assistance with household activities for individuals with disabilities? 1. III. What is an appropriate age for reduction and eventual cut-off of household services due to aging? This article provides an overview of currently available American and Canadian Time Use Survey data and its application to life care planning in each of the three areas outlined above. In some parts of Canada, a number of life care planners are currently using time use survey data to assist with determining the amount of funding to allocate for household services. When used appropriately, such data and its activity classification systems can take the guesswork out of determining what type and amount of household services should be allocated for clients who are limited from performing such activity due to their disability. A case study also is offered.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".