Tennessee Dental Establishments: 2007-2012 Economic Survey.
Bibliographic record
Abstract
OBJECTIVE: To document the evolving economies of dental establishments in the State of Tennessee during a period of significant growth of the number of establishments before and after the "Great Recession." METHOD: Published results from the five-year economic surveys by the U.S. Census Bureau on business receipts and salaries of employees (including dentists) for the State of Tennessee and its counties were used to construct a review of these developments. RESULTS: Between 2007 and 2012, there were continued increases in average current dollar business receipts and employee salaries. However, in terms of standard dollars, removing the effects of inflation: 1) business receipts increased in 19 counties but decreased in 37 counties; 2) employee salaries increased in 13 counties but decreased in 30 counties. CONCLUSIONS: Results are in line with the reports by the ADA Health Policy Institute, which indicate that nationally the percentage of dentists who report they are not busy enough has increased and dentists' earnings are stagnating. The need is to expand the delivery of care to underserved populations, including the poor, individuals with disabilities, minorities and new immigrant populations, for whom oral health services may not be a priority commodity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".