Continuity in primary care and its impact on investments in health capital. an application and extension of the grossman model of the demand for health
Bibliographic record
Abstract
This thesis examines continuity in primary care within the framework of an economic model of health. Grossman's Model of the Demand for Health explores how individual factors such as education affect the efficiency in the production of health investments and the resulting addition to one's stock of health. This thesis posits that continuity of care also increases the efficiency of investments in health and uses this theoretical framework to look at continuity of care and its impact on the demand for health. The main objectives of this study were to: determine what individual patient characteristics are associated with continuity of care; assess the relationship between continuity of care and health status; assess the relationship between continuity in primary care and ambulatory care utilization; and study the effect of the presence of a chronic condition. The study used data from the 1994 National Population Health Survey and linked it ambulatory care and hospitalization data. Continuity was measured by Bice and Boxerman's COC index. The analyses included multivariate regression to model continuity of care with individual-level variables and ambulatory care utilization. Stratification on the basis of whether or not a patient had a chronic condition was also used to look at the effect of this variable on the relationship in question. A strong relationship was found between continuity in primary care and patient age, retirement and income. Increasing continuity was associated with decreasing rates of medical hospitalizations, indicating a possible relationship between continuity and health. A negative association was found between continuity in primary care and the number of visits made per patient. This relationship was not affected by individuals' chronic disease status. Expenditures appeared to be unrelated to continuity of care, except among patients with chronic conditions, where the relationship was found to be negative. This study demonstrated an association between continuity of care and the demand for medical care. These findings support the hypothesis that with greater continuity, patients realize greater returns on the investments in their health capital. As a result, they derive more health benefits and experience a reduced demand for health and for further health care.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".