Retrospective and Forecasting Analysis of Increased Long Term Care Demand in Niagara
Bibliographic record
Abstract
A problem exists within the Canadian healthcare system as many patients experience longer lengths of stay (LOS) in acute care (AC) and complex care (CC) beds within hospitals because of a lack of long term care (LTC) facilities. The purpose of this study was to evaluate the extra days patients wait for placement and assess the benefits of increasing the number of LTC beds. The theoretical framework used was the four-level model of the health care system. Research questions involved 2017-2019 data for the number of LTC beds required to eliminate waits and evaluate beds needed in the future. This study involved using a retrospective quantitative study using hospital-acquired deidentified data from Ontario. Data were input into a forecasting model to assess the number of LTC beds required and forecast the number of beds needed to address future demand. Data demonstrated both a seasonal and periodic increase and indicated the problem would continue to escalate into 2027. In 2019, an average of 120 patients were waiting in hospital for an end destination LTC, and by 2027, if nothing changes, data showed that the number of patients waiting will increase to 509. Results showed that 12 of the 25 medical complexities patients had on their profile predicted extended waits in the hospital for a LTC bed. This study can impact positive social change by advocating for an increase in LTC beds, allowing patients who wait to discharge to appropriate settings timely and better allocation of healthcare dollars in Ontario.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".