Allocation of Dentists to Canadian Armed Forces Dental Clinics: A Comparison between Two Human Resource Planning Models
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. \nMethods: 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 of the number of dentists allocated. \nResults: Under the current dentist-to-population ratio model, 83.25 FTE general dentists are allocated to CAF detachments compared to 64.71 FTEs that would be allocated using a needs-based model. The average difference between models was 0.71 FTEs. \nConclusions: The results should be interpreted with caution since extrapolating treatment needs to the population-level using limited treatment plan data could lead to an over- or underestimation of human resources requirements. If the results are replicated in future research, then changing to a needs-based model may result in cost savings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".