Design of long-term care facility networks
Bibliographic record
Abstract
The objective of this thesis is to provide decision makers with a tool to assist in the development of short-, medium- and long-term capacity planning for long-term care networks. Although this research may be applied to other service sectors, as well as others areas of interest to those in the operations research community, the application area of this thesis is restricted to the domain of long-term care. When designing long-term care networks for the elderly and infirm, a network should both satisfy demand in a timely fashion and take a patient's perceived quality-of-life into account. As these elderly, infirm patients make this final transition to long-term care, it is incumbent on healthcare providers to analyze and evaluate the balance between a patients' needs for care versus cure. This thesis provides decision makers with two distinct network-design tools. The first methodology addresses the imminent surge in demand for long-term care services, and how best to build up the long-term care network to accommodate patients in manner that is sensitive to their perceived quality-of-life. The second methodology is forward-looking, examining how the current long-term care network can be improved with additional levels of care, and how the proposed changes affect cost and patients' perceived quality-of-life. The quantity, size, location, type of care and patient perceived quality-of-life are the main determinants of the network configuration for both of the aforementioned network-design tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".