Measuring Health Services Utilization in Ethnic Populations: Ethnicity and Choice of Frameworks
Bibliographic record
Abstract
Background: Health services utilization (HSU) is an important health outcome indicator, a surrogate measure of access to healthcare, that influences the outcomes of health status and consumer satisfaction, is a surrogate measure of access to healthcare.To most healthcare providers, understanding patterns of HSU is important for health system resource planning and allocation.In studies of HSU, the ethnicity variable is key especially in multicultural societies such as Canada where a significant proportion of the population consists of visible minorities, who, often are referred to in research studies as 'underserved' or as 'hard-to-reach' in the sampling context.Approach: This article discusses the relevance and utility of some frameworks for studying access to healthcare.It is intended to set the stage for a future study comprehensive systematic literature review of models of access to care and models of health system performance that address access as a criterion.To achieve this, I present selected published research on frameworks for access to medical care and HSU, including their characteristics and appropriateness for health services research.Subsequently, I briefly examine two studies that explore HSU, in Canada and the United States to determine the extent to which the selected framework or its components were employed.Finally, I describe ethnicity as a variable in the framework for HSU and conclude with limitations of this review.Conclusions: This paper has highlighted the approaches to health services utilization and the application of the Aday-Andersen framework for studies on access to care.Although this paper draws on relevant and some important contributions in the field, the assessment presented here is not exhaustive and warrants a more comprehensive review that includes recent literature on the subject.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.070 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".