Is there a sound basis for deciding how many dentists should be trained to meet the dental needs of the Canadian population? Systematic review of literature (1968-1999).
Bibliographic record
Abstract
A systematic review was conducted of the literature on human resources planning (HRP) in dentistry in Canada, critically assessing the scientific strength of 1968-1999 publications. Inclusion and exclusion criteria were applied to 176 peer-reviewed publications and "grey literature" reports. Thirty papers were subsequently assessed for strength of design and relevance of evidence to objectively address HRP. Twelve papers were position statements or experts' reports not amenable for inclusion in the system. Of the remaining 18 papers, 4 were classified as projections from manpower-to-population ratios, 4 as dental practitioner opinion surveys, 8 as estimates of requisite demand to absorb current capacity and 2 as need-based, demand-weighted studies. Within the 30.5 years reviewed, 53.4% of papers were published between 1982 and 1987. Overall, many papers called for a reduction in human resources, a message that dominated HRP during the 1980s, or noted an increase in the demand for services. HRP publications often had questionable strength or analytic frameworks. The paradigm of busyness-scarcity evolved from a belief around an economic model for the profession into a fundamental tenet of HRP. A formal analysis to establish its existence beyond arbitrary dentist:population ratios has usually been lacking.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".