Residential Long-Term Care Capacity Planning: The Shortcomings of Ratio-Based Forecasts
Bibliographic record
Abstract
This paper uses observations from two British Columbia studies to illustrate the shortcomings of widely used ratio-based approaches for residential long-term care capacity planning. It shows that capacity plans based on a fixed ratio of beds per population over age 75 may result in either excess capacity or long wait times for admission. It then investigates the use of linear regression models to obtain a "best" ratio by relating optimal plans derived by rigorous analytical methods to population characteristics and shows that no single ratio applies broadly. While the use of regression is promising, finding these "best" ratios is too analytically complex for general practice. The paper concludes by providing and evaluating an easy-to-use planning method, which we call the average flow model (AFM). The AFM combines demand forecasts with length-of-stay estimates to produce enhanced capacity plans. The AFM is transparent, easily implemented in a spreadsheet and well suited for "what if?" analyses.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".