Bibliographic record
Abstract
Disparities in health and healthcare plague provinces' residents.This means that different subgroups of the population have differences in health and healthcare.Examples include socio-economic status, location of residence and disability status.Disparities generate searching questions: does where you live, or who you are, affect your health or the quality or accessibility of healthcare you receive?It is important for provincial ministries of health and social care to address the issue of disparities.Disparities limit gains in health among the population, induce additional healthcare utilization or spending and lay bare inequitable distribution of public resources.Resolving disparities is often challenging as causal pathways may be complex, ingrained in communities or very expensive to address.Research on disparities is an important element of improving provinces' population health.Quantitative and qualitative researchers play complementary roles of observers and reporters of often-in-plain-sight disparities.Policy analysis plays the adjunct role of exploring intersections of legislation, strategy and program objectives and identifying short-and longterm options for decision-makers.In this issue of Healthcare Policy, several papers identify and examine disparities in access to healthcare services.Tobias and colleagues (2020) examine the effectiveness of cancer screening programs among First Nations peoples, Jones and colleagues (2020) report on gender differences in access to surgery, whereas Martin-Misener and colleagues (2020) explore facets of rural and remote healthcare delivery through the role of nursing practices.This issue also features an interprovincial comparison of childhood cancer costs by McBride and colleagues (2020), which provides valuable insights into variations in care delivery.Zoratti and colleagues (2020) report on a three-province comparison of drug reimbursement recommendations, whereas Bell and colleagues (2020) explore equity in Kicking Off the 2020s with Healthcare
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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 | 0.043 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.025 | 0.027 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".