Bibliographic record
Abstract
Objectives: By 2050 it is expected that the population of individuals over the age of 80 will increase from 14.5 million to 394.7 million. During this time, chronic diseases will account for over half of the low to middle income countries disease burden. These in combination have led to increased stress on long term care (LTC) facilities to accommodate the elderly population. The objective of this paper is to identify the stakeholders, barriers and solutions to better managing wait-times in long-term care (LTC) facilities in Ontario. Background: Current research identifies practical solutions to prolonged wait times for long-term care facilities. There is a gap in identifying the stakeholders and barriers that affect long-term care wait times. Importance we are filling the gaps in research by identifying stakeholders and solutions to barriers that can improve wait times in long-term care facilities for the elder population. Methods: We reviewed a variety of scholarly, news and statistic based articles. We utilized the search engine Cumulative Index to Nursing and Allied Health Literature database (CINAHL) between the years 2012-2017 to insure up to date research is utilized. Both qualitative and quantitative research was reviewed based on studies related to LTC facilities. Results: Wait times for admission to a LTC facilities in Ontario averaged 99 days. The population of individuals over 80 is expected to rise by 380.2 million (272%) by 2050. This places a strain on admission times into a LTC facility, which increases the demand for beds. The greatest barriers were a lack of available beds and the large expense of placing a family member in a LTC facility. We also identified internal and external stakeholders. Results demonstrated that if stakeholders worked together wait times could be decreased. Strategies to reduce wait times include implementing unique approaches such as increasing investments in home care, promoting mobilization and nutrition, and preventing chronic diseases. These actions have the potential to decrease overall LTC facilities wait times. Keywords: Long-term care, Home, Wait-time, Stakeholder, Barriers, Health.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".