Bibliographic record
Abstract
� In 2010, about 13 % (OECD average 15%) of Canada’s population is aged over 65 and about 3.5 % (OECD average 4%) over 80. � In 2006, Canada’s expenditure on long term nursing care was equivalent to about 1.5 % of its gross domestic product (GDP). More than 80 % of these expenditures were targeted to institutional care (OECD Health Data, 2010). � In 2008-09, about 0.7 % (250,000 individuals) of the Canadian population resided in an institution, of which about 75 % were 65 years and older. The 238,000 individuals are equivalent to about 4 % of the population over 65. � In 2008-09, there were approximately 4 850 residential care facilities across Canada with 270 000 approved beds. Of these beds, about 217 000 were approved for homes for the aged (Statistics Canada, 2008-2009). � In 2006, more than 2.5 % (875,000 individuals) of the population reported receiving home health care and home support; about 60 % of this group received home health care only (CIHI, 2007). � In 2006, about 160,000 nurses and personal carers worked in the long-term care (LTC) sector on a full-time basis and close to 70,000 on a part-time basis (OECD Health Data, 2010 based on
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.315 | 0.096 |
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".