Bibliographic record
Abstract
Data from the Canadian Institute for Health Information (CIHI) indicate that the average length of a hospital stay in Canada dropped by more than 5% between 1994/95 and 1997/98, falling from 7.4 days to 7.0 days. The age-standardized discharge rate (a measure of Canadians‚ in-patient use of hospitals) fell by 13.8%, from 11 499 discharges per 100 000 population in 1994/95 to 9913 per 100 000 population in 1997/98. When combined, these 2 figures point to a 15% decrease in total patient days between 1994/95 and 1997/98. All jurisdictions except the Northwest Territories experienced a decrease in discharge rates between 1996/97 and 1997/98, with Ontario showing the greatest decrease (5.3%). In 1997/98, the highest discharge rates per 100 000 population were found in New Brunswick (14 304), Saskatchewan (14 171) and the NWT (13 937), while Ontario‚s rate was the lowest &mdash% 9530 per 100 000 population. Women accounted for slightly more than half (51.1%) of nonpregnancy and childbirth-related hospitalizations in 1997/98. Heart disease and stroke were the leading cause of hospitalization for both males (20.5%) and females (15.1%), followed by digestive diseases (13.1% of hospitalized women, 12.6% of men). In 1997/98, patients 65 and older accounted for 34.7% of all hospitalizations, and stayed in hospital an average of 10.5 days. In contrast, adults between the ages of 20 and 64 stayed an average of 5.4 days. Hospital stays for children and teenagers lasted an average of 4 days.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".