Self-report and performance measures differ in their association with home care use : an exploratory study
Bibliographic record
Abstract
Functional status measures are used extensively to determine the home care needs and eligibility of older adults.However, it is unclear what type of functional status measure is best for these objectives.The purpose of this secondary analysis of the 2001 Aging in Manitoba t ongitudinal Study survey and linked Manitoba Health administrative data was to investigate the concurrent and longitudinãl relationship between three different measures of functional status and the formal home care use of older Manitobans.Analysis was structured with the Andersen-Newman Framework of Health Services Utilization (1973) as a guide.ln these small exploratory study results, the self-report of capacity measure appeared most associated with home care use cross-sectionally, while the performance measure was best able to predict home care use two and a half years following the functional status assessment.Results emphasize that different types of functional status measures are not interchangeable.the MDS-HC tool has resulted in increased thoroughness of home care case coordinator assessments.Chapter Summary FunctionalStatuSaSSeSsmentsareuseddailybyvarioushealthcareprofessionals to assess a person's need for home care services, and have potential benefits as a measure of future home care needs in community needs assessments.However, functional status is measured in many different ways and the relationship between different types of : functional status measures and home care services is not yet well understood.The 2001 AIM survey and linked administrative data contains information that can be used for an exploratory investigation of the relationship between three different functional status measures and the use of the Manitoba Home Care Program, which is a formal home care program provided to Manitobans in their own homes.It is anticipated that understanding how different types of functional status measures are able to differentiate between home care users and non users will assist clinicians and researchers with choosing the most efficient and accurate functional status measure.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".