Rheumatology Workforce Planning in Western Countries: A Systematic Literature Review
Bibliographic record
Abstract
OBJECTIVE: To compare health care planning models forecasting rheumatology workforce requirements in western countries. METHODS: A systematic literature review was conducted through medical databases (Ovid MEDLINE, Embase, CINAHL, and Cochrane Library) and the grey literature. All articles reporting a rheumatology workforce model were included. RESULTS: The search yielded 6,508 articles, and 14 publications (on 12 studies) were included. Workforce models were available for the US (n = 3), Canada (n = 3), the US plus Canada (n = 1), Germany (n = 2), Spain (n = 1), and the UK (n = 2). The number of rheumatologists required to serve a population of 100,000 people was calculated, with a range of 0.7 (UK, calculated for 1988) to 3.5 (Spain, calculated for 2021). Most models used a needs-based approach (n = 6); 3 studies each applied a supply- or demand-based method. The following variables were considered by ≥1 model: disease prevalence, patients' referral to rheumatologists, clinical visits/patient/year, population development, factors influencing performance of rheumatologists, patient flow/care sharing, and medical technologies/infrastructure development. CONCLUSION: Heterogeneity in methods used, the period or calendar years for which the estimates were projected, and heterogeneity of variables evaluated led to disparate estimates, with results ranging from 0.7 to 3.5 rheumatologists per 100,000 population. An international initiative is needed to agree upon a common approach for a reliable estimation of manpower requirements in rheumatology.
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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 | 0.015 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".