Understanding the demographic characteristics and health of medically uninsured patients.
Bibliographic record
Abstract
OBJECTIVE: To determine demographic and diagnostic information about the medically uninsured patient population and compare it with that of the medically insured patient population at a primary care centre. DESIGN: Medical chart audit. SETTING: Department of Family and Community Medicine at St Michael's Hospital in Toronto, Ont. PARTICIPANTS: Medically uninsured patients who were treated in the Department of Family and Community Medicine at St Michael's Hospital from 2005 to 2009, as well as randomly selected patients who were insured through the Ontario Health Insurance Program. MAIN OUTCOME MEASURES: The following information was obtained from patients' medical charts: patient's age, sex, and household income; if the patient had a specific diagnosis (ie, hypertension, type 2 diabetes mellitus, HIV, tuberculosis, substance addiction, or mental health disorder); if the patient accessed a specific category of primary care (ie, prenatal care or routine pediatric care); and the reason for the patient's uninsured status. RESULTS: There was no significant difference in the mean age and sex distribution of insured and uninsured patients. The uninsured group had a significantly lower mean household income (P = .02). With the exception of HIV, there was no significant difference in the prevalence of the specific diagnoses studied or in the prevalence of accessing specific categories of primary care between insured and uninsured patients (P > .05). The prevalence of HIV was significantly greater in the uninsured group (24%) than in the insured group (4%) (P = .004). The largest proportion of uninsured patients lacked health insurance owing to the landed immigrant health insurance waiting period (27%), followed by individuals without permanent resident status in Canada (22%). A subgroup analysis of the uninsured, HIV-positive population revealed that the largest proportion of individuals (36%) lacked health insurance because they had no permanent resident status in Canada. CONCLUSION: Uninsured and insured patients at the primary care centre did not differ significantly with respect to age and sex distribution; prevalence of hypertension, type 2 diabetes mellitus, tuberculosis, substance addiction, or mental health disorder; or the proportion who sought prenatal or routine pediatric care. The landed immigrant 3-month waiting period was the most common reason that uninsured patients lacked health insurance. Uninsured patients in this study lived in lower-income areas than insured patients did. This, combined with the increased prevalence of HIV in the uninsured group, might lead to a large number of uninsured, HIV-positive patients delaying seeking treatment and might have negative implications for public health.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".