Healthcare utilization among adults with a history of homelessness: The distribution and determinants of high-cost use of the healthcare system
Bibliographic record
Abstract
People experiencing homelessness face a multitude of health challenges that increase their need for acute health services. While there is not a one-size fits all approach to end the homelessness crisis in Canada and abroad, the healthcare system offers an important point of contact to intervene. This dissertation offers an in-depth view of the distribution and determinants of healthcare use among adults with a history of homelessness to inform the development of tailored housing and support strategies. Survey data from two large cohorts of homeless adults were linked with healthcare records under a single-payer healthcare system. These data represent a cohort of adults with a history of homelessness and a subgroup of homeless adults with a mental illness. Data linkage offers a unique opportunity to combine survey and administrative records to overcome the limitations of using each data source alone. Throughout this dissertation, the findings from three studies are described in detail. The first study offers a complete assessment of health care encounters (hospitalizations, emergency department visits, and physician visits) and applies the Behavioral Model for Vulnerable Populations to identify the predisposing, enabling, and need factors associated with higher use of these health services. The second study uses a novel approach to study healthcare costs within the homeless population by using previously established cut-offs from the general population to classify healthcare users in the top 5%, top 6-10%, top 11-50% and bottom 50% of total cost users. This approach permits comparisons in the distribution and determinants of higher cost use across the cohorts and with the general population. The third study informs the use of often arbitrary frequent user definitions by comparing health care encounters and costs across the commonly applied classifications. This work also advances research on high-cost healthcare use among adults with a history of homelessness in absence of validated cost data by validating a new method of using encounter data to identify high-cost users (top 5%). This dissertation provides insight into the use of health services within the homeless population to inform tailored housing and support strategies for people who use the health system while homeless.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".