The impact of preventive health behaviour and social factors on visits to the doctor
Bibliographic record
Abstract
BACKGROUND: The aim of this study is to examine the joint impact of preventive health behavior (PHB) and social and demographic factors on the utilization of primary and secondary medical care under a universal health care system, as measured by visits to the doctor, who were categorized as either a General Practitioner (GP) or Specialist Doctor (SD). METHODS: An ordered probit model was utilized to analyze data obtained from the 2009 Israeli National Health Survey. The problem of endogeneity between PHB factors and visits to GP was approached using the two-stage residuals inclusion and instrumental variables method. RESULTS: We found a positive effect of PHB on visits to the doctor while the addition of the PHB factors to the independent variables resulted in important changes in explaining visits to GP (in values of the estimates, in their sign, and in their statistical significance), and only in slight changes for visits to SD. A 1% increase in PHB factors results in increasing the probability to visit General Practitioner in the last year in 0.6%. The following variables were identified as significant in explaining frequency of visits to the doctor: PHB, socio-economic status (pro-poor for visits to GP, pro-rich for visits to SD), location (for visits to SD), gender, age (age 60 or greater being a negative factor for visits to GP and a positive factor for visits to SD), chronic diseases, and marital status (being married was a negative factor for visits to GP and a positive factor for visits to SD). CONCLUSIONS: There is a need for allowing for endogeneity in examining the impact of PHB, social and demographic factors on visits to GP in a population under universal health insurance. For disadvantaged populations with low SES and those living in peripheral districts, the value of IndPrev is lower than for populations with high SES and living in the center of the country. Examining the impact of these factors, significant differences in the importance and sometimes even in the sign of their influence on visits to different categories of doctors - GP and SD, are found.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".