IMPROVING LONG TERM CARE IN CANADA
Bibliographic record
Abstract
Within the next 20 years we will see a drastic increase in the number of seniors, which will make up more than 25% of our population (Statistics Canada projection 2014). This will lead to a significant pressure for improved geriatric health care needs. Although the Canadian Government faces many challenges in trying to balance budgets amongst infrastructure, other social welfares the government needs as well as healthcare, this incoming societal change requires us to subsidize elder care. Beyond health care, reduction of income upon retirement also interferes with the basic needs of life. We need stronger social welfare services for the elderly in Canada. We must expand income assistance for seniors beyond OAIS (Tridelta Financial 2014), the CPP (Government of Canada 2017), and guaranteed income supplement. Although these assistance programs are extremely beneficial to niche senior groups, in the future, we will be seeing a rise in number of seniors in every economic class which will require broader infrastructural improvements. This article will outline how the Canadian Government should expand their social welfare program and medical coverage for the health of seniors to: improve long term care insurance availability, expand at home care funding, and make specialty, palliative and primary care more accessible/affordable.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".