Unmet health care and health care utilization
Bibliographic record
Abstract
The objective of this study is to examine the causal effect of health care utilization on unmet health care needs. An IV approach deals with the endogeneity between the use of health care services and unmet health care, using the presence of drug insurance and the number of physicians by health region as instruments. We employ three cycles of the Canadian Community Health Survey confidential master files (2003, 2005, and 2014). We find a robustly negative relationship between health care use and unmet health care needs. One more visit to a medical doctor on average decreases the probability of reporting unmet health care needs by 0.014 points. The effect is negative for the women-only group whereas it is statistically insignificant for men; similarly, the effect is negative for urban dwellers but insignificant for rural ones. Health care use reduces the likelihood of reporting unmet health care. Policies that encourage the use of health care services, like increasing the coverage of public drug insurance and increasing after hours accessibility of physicians, can help reduce the likelihood of unmet health care.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".