The Influence Of Access on the Use of Specialists Health Care in Norway
Bibliographic record
Abstract
This paper studies the extent to which the principle of “equal access” and “equal use for equal need” is maintained in the specialist health care delivery system of Norway. We include three types of specialist health care services: hospital inpatient stay, hospital outpatient visit and private specialist visit. We investigate inequality in access with accessibility indices that combine rich information on the capacity of specialist health care and the distance from residence to the hospital and private specialist care. We investigate inequity in the use of specialist health care with data from the 2008 Survey of Living Conditions linked with data on access to specialist health care (accessibility indices). We find inequality of access to specialist health care revealing that the capital Oslo has the best access to specialist health care and the residents of northern Norway (Finnmark county) has the worst access. Moreover, we find inequities in use of hospital inpatient stay with respect to ethnicity and education, in use of hospital outpatient visit with respect to education and access to private specialist and in use of private specialist visit with respect to education, household income and the access to private specialists. We find that the better access to private specialists is, the higher is the probability of visit to a private specialist. Regarding hospital outpatient we find that the better access to private specialists is, the lower is the probability of a visit to hospital outpatient clinic. This suggests that the use of a hospital outpatient visit is a possible substitute for private specialists. We consider this study to be helpful in identifying how equitable specialized health care are distributed and in developing future health policies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".