Bibliographic record
Abstract
INTRODUCTION The Commonwealth of Australia is one of the wealthiest countries in the Western Pacific region, with a population estimated at over 23 million in 2013.1 About 27% of the population were born overseas, predominantly in the United Kingdom, New Zealand, China, and India,2 and 3% of the population are Indigenous Australians.3 Despite the country’s geographic size, Australia’s population is substantially lower than that of other regions of similar size4 because of a large, unin habitable central desert. Australia is divided into 6 states and 2 major mainland territories (Figure 1); in most respects, the 2 territories function as states. According to the Australian Bureau of Statistics, life expectancy is among the highest in the world and 25 years longer than a century ago. A baby boy born between 2010 and 2012 can expect to live to 79.9 years of age and a baby girl to 84.3 years;3 however, life expectancy for the Indigenous population is 10.6 years less for boys and 9.5 years less for girls. Like most developed nations, Australia has experienced a drop in birth rate and infant mortality. More specifically, infant mortality rates have fallen from 65.7 to 3.3 deaths per 1000 live births over the past 85 years.5,6 Decreased birth rates coupled with increased life expectancy mean that Australia exemplifies global trends, with an expanding older population. This article is the first in the series “International Perspectives on Pharmacy Practice”. For general information about the series, see the article elsewhere in this issue: Raman-Wilms L, Moles RJ. Widening our horizons: pharmacy practice from a global perspective. Can J Hosp Pharm. 2015;68(5):417. WHO Region:The Western Pacific Country: Australia
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.128 | 0.020 |
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".