Comparison of pediatric dental practitioner workforce in the midwestern United States: 1990 and 2000.
Bibliographic record
Abstract
PURPOSE: The objective of this study was to detail a state-based comparison of the pediatric dental practitioner workforce in the midwestern United States between 1990 and 2000. METHODS: Enumeration of pediatric dental practitioners was derived from the American Academy of Pediatric Dentistry's 1990-1991 and 2000-2001 membership directories. Included in the study were all active and fellow members in private practice in the 8 midwestern states of: (1) Illinois; (2) Indiana; (3) Iowa; (4) Michigan; (5) Minnesota; (6) Missouri; (7) Ohio; and (8) Wisconsin. Analysis of state-based practitioner cohorts included determination of: (1) individual practitioners who did not practice in 1990 but were practicing in 2000 (addition); and (2) those who practiced in 1990 but who did not practice in 2000 (attrition). RESULTS: The number of pediatric dental practitioners in the 8 midwestern states showed a net increase (24%) from 406 to 504 between 1990 and 2000, with Illinois showing the highest increase (45%) and Iowa the lowest (4%). There were 218 individuals (54%) added to the pediatric dental practitioner workforce in the midwestern states from 1990 to 2000, with an attrition of 120 individuals (30%). CONCLUSIONS: The pediatric dental practitioner workforce in the midwestern United States showed a net increase and sizeable turnover between 1990 and 2000.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".