Predicting the utilization of home care and personal care homes by community-dwelling older Manitobans
Bibliographic record
Abstract
Understanding the reasons why older adults use home care and personal care homes (PCHs) is necessary to manage the care and costs of these health care services, The present research has used a representative sample of older adults from Manitoba, Canada, to compare how prevalence and incidence risk factors predict the use of home care and PCHs.The Andersen- Newman Model of Health Care Utilization was incorporated into analytic strategies, and was used to help interpret study results.Study participants completed the 1983 and 1990 Aging in Manitoba (AlM) interviews, and participants' responses to these questions were used as potential risk factors.These AIM data were linked to the government of Manitoba administrative health records that described participants' use of home care and PCHs, Prevalence and incidence risk factor data were used to develop four statistical models to determine participants' risk of using these health care services, This research has helped to define users of home care and PCHs.People with functional limitations were more likely to use PCHs, and those with chronic health conditions and physical impairments were more likely to use home care.Participant age and sex, and indicators of social supports indirectly influenced the use of these health care services.Males and younger people with a health limitation were more likely to use home care, while females and people age 85 years and older were more likely to use PCHs.lndividuals with fewer social supports were more likely to use home care and PCHs.Study results were more strongly demonstrated with incidence data, and the sequence of events leading to the use of home care and PCHs was also better defined with these time sensitive measures, The Andersen-Newman model helped to understand how different risk factors influenced the use of home care and PCHs.However, as has been noted by other researchers, it remains difficult to accurately predict the use of these health care services.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".