Study of ambulatory physician utilization in St. John's, Newfoundland
Bibliographic record
Abstract
The primary objective of this research was to study the demographic and socioeconomic factors thought to influence the type and level of ambulatory visits by individuals 20 years and older residing in St. John's, Newfoundland, Canada. The data were derived from the Newfoundland Panel on Health and Medical Care, a provincial study involving some 12,000 residents from which a sub-sample of 2861 adults residing in St. John's was analysed. The study combined a cross sectional survey with a longitudinal panel for physician utilization during 1992-95. -- A descriptive analysis of demographic variables (gender, age), socio-economic variables (education, income), and health status variables (self assessed health status and a number of chronic conditions) was conducted. Multivariate analysis was used to clarify the complex association between the selected variables and ambulatory physician utilization. Binary logistic regression techniques were first undertaken to predict the number of visits to both general practitioners and to specialists, and finally ordinal logistic regression was used to determine appropriate models for predicting the number of visits to general practitioners and specialists. -- Study results were consistent with findings in similar studies. Females are almost twice as likely to have a high number of visits to general practitioners than males, utilization increases with age, individuals with low socio-economic status (SES) scores, poor health status, and more than one chronic condition visited general practitioners more frequently. Although such patterns of utilization were found to be similar to those of specialists, some differences were noted. The study concludes that age, gender, income, education, health status, and the presence of chronic conditions were deemed to be good predictors of ambulatory physician utilization in the St. John's Institutional Board Region during the three year study period.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".