Bibliographic record
Abstract
data described so far, and then adjusting for the mixture of factors just mentioned – including chronic disease prevalence, and risk factors including smoking and obesity, is shown by the next steepest line. These statistical adjustments reduce the 90th to 10th percentile regional hospitalization ratio a bit more, down to 2.0. Finally, there are further, albeit more distal, socio-economic health determinants which might also account for some of these large differences in hospitalization rates across health regions in Canada. To account for this, the least steep line incorporates further statistical adjustments for these socioeconomic status (SES) factors – including income, education, race, and immigration status. The 90 – 10 hospitalization ratio now declines further from 2.0 to 1.7. Interestingly, this last adjustment has about the same impact as the first two sets of adjustments combined – age and sex, and illness, risk factors and other health care use. Compared to the early 1990s when the idea of the social determinants of health having a major role in understanding why some people are healthy and others not6 was still a contested academic curiosum, it is now widely accepted. The results in this graph clearly reinforce this substantive point. But after almost two decades of discussion and effort, it still has not penetrated to the structure of Canada’s health information to any substantial degree. Chart 1 required major, special efforts, and these kinds of data are not routinely produced. Moreover, these statistical adjustments do not make the wide variations in hospitalization rates go away. Indeed, we may have over-adjusted. So there must be an important range of other factors – presently unknown – driving such large variations in utilization of one of the most expensive parts of Canada’s health care sector. Similar analysis in the United States using their national Medicare data clearly indicated that the observed 3:1 small area variations indicated major inefficiencies, and these results have been central to their recent health care reforms (Fisher et al., 2003; Gawande, 2009; Gawande
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.086 | 0.030 |
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".