The Impact of Social and Material Inequities on Health Care in the Erie-St. Clair LHIN
Bibliographic record
Abstract
Despite evidence that investing in health equity increases overall population health for a fraction of the cost of investing in acute care and medical treatment, only five percent of Canadian healthcare dollars are allocated for public health, a fraction of which is spent on addressing health inequity. The purpose of this research study is to examine the impact of social and material inequities on healthcare in the Erie-St. Clair Local Health Integration Network (LHIN), with a focus on the Social Determinants of Health. During the review of the literature, it was found that overall materially and socially deprived patients fared much worse in all aspects of healthcare, including access, utilization, outcomes and cost, than those in who were not deprived. In fact, positive healthcare outcomes increase steadily as one moves up the spectrum from the most deprived to the least deprived. No studies were found that examined this occurrence in the Erie-St. Clair LHIN. This research study will enable the LHIN to focus their efforts on the specific Social Determinants that are affecting healthcare in Erie-St. Clair. This will be a mixed-methods study including archived data analysis to aggregate data on a population level and qualitative in-person interviews and telephone surveys to establish individual level data with residents and stakeholders. This funded study is currently in the planning stages, and no conclusions or results are evident yet. However, based on the literature, it is evident that there is an absolute need for investment in health equity and the socioeconomic model of healthcare versus the medical model.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".