Using location-allocation models to aid in the locating of preventive health care facilities for Newfoundland & Labrador
Bibliographic record
Abstract
The province of Newfoundland and Labrador is facing unprecedented demographic \nchange. The population is aging at a faster rate than in any other province in Canada, and \nthis is leading to dramatically increased costs to the province’s health care system. One \nway to help alleviate the rising costs of health care is to promote preventive health care. \nPreventive health care can save lives and contribute to better quality of life by diagnosing \nserious medical conditions early. Unlike services for those who have urgent medical \nneeds, preventive health services are intended primarily for healthy people who are less \nwilling to travel long distances to access services. For this reason preventive health \nservices, such as mammography units, require different locational decision methodology \nthan other types of health care (Gu & McGregor, 2010). \nThis research provides a methodology to locate preventive health care facilities \nefficiently while ensuring spatial equity in distribution of services. Spatial equity refers \nto the locating of services for individuals equitably regardless of where they live. To \nachieve this, a variation is presented on the traditional maximal covering location \nproblem that incorporates equity into location-allocation (LA) modeling. Using custom \ndeveloped LA software, the variant algorithm is used to locate mammography facilities as \na representative type of preventive health services for the island of Newfoundland. The \nsolution set is compared to the locations of the current mammography program, which \nwill show that the facilities of the province are well located. The results are compared to \nthose of other models and shown to be the best in terms of equity in service delivery. This study also helps demonstrate that LA models are an effective tool in public facility \nplanning, especially when evidence-based decision making is important.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".