Comparing human resource planning models in dentistry: A case study using Canadian Armed Forces dental clinics
Bibliographic record
Abstract
OBJECTIVES: To compare two methods of allocating general dentists to Canadian Armed Forces (CAF) dental detachments: a dentist-to-population ratio model and a needs-based model. METHODS: Data obtained from CAF sources were analysed to compare models. Times assigned to treatment plan procedures were used as a proxy for treatment needs. Full-time equivalents (FTEs) were used as an indicator for the number of dentists allocated to each detachment. FTE values were adjusted for military dentists to account for time spent on compulsory nonclinical duties. The paired-samples t test was used to assess differences between the models for all clinics (dental detachments) and by clinic size. RESULTS: The dentist-to-population ratio model for the CAF population (n=68 183) estimated an allocation of 83.25 FTE general dentists to CAF dental detachments. Based on a systematic sample of the CAF population (n=2226), the needs-based model estimated the requirement for 64.71 FTE general dentists. The average difference between models was 0.71 FTE (SE=0.273), which was statistically significant (P=0.015). In terms of differences by clinic size, differences were more pronounced in clinics serving more than 4000 CAF personnel (2.63 FTEs, SE=0.613, P=0.008). CONCLUSIONS: The findings reveal differences between estimation models of <1 FTE, with higher estimates produced from the dentist-to-population ratio model. A larger difference was found in clinics with larger populations. The perceived overestimation of dental human resource requirements suggests that changing to a needs-based model may result in cost savings.
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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 | 0.027 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".