Planning the Restructuring of Long-Term Care: The Demand, Need and Provision of Institutional Long-Term Care Beds in Newfoundland and Labrador
Bibliographic record
Abstract
The Canadian population is aging. In Newfoundland and Labrador, nursing homes and supervised care facilities provide Long-Term Care (LTC). There may be a mismatch between the provision of LTC beds and clients' needs. To compare the type and annual rate of clients seeking placement to LTC, incident annual cohorts (N = 1,496) in five provincial health regions within Newfoundland and Labrador were compared using objective measures of disability. Client need was assessed using a decision tree and the optimal distribution of LTC beds was determined. Within the four island regions, little difference was observed in degree of disability, but Labrador clients differed from the island regions in age, degree and type of disability. A decision tree suggested that optimal placement was 7% to housing, 34% to supervised care, 17% to supervised care for cognitive impairment and 42% to nursing home care. In Newfoundland and Labrador, institutional LTC is dependent on nursing homes, whereas the major need is for appropriate supervised care for those with modest disability, with or without cognitive impairment. Different approaches to restructuring of long-term care in each region are necessary because of the differences in rates of presentation for LTC and differences in availability of nursing home and appropriate supervised care beds.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".