Variations in Pediatric Rheumatology Workforce and Care Processes Across Canada
Bibliographic record
Abstract
OBJECTIVE: To examine the Canadian pediatric rheumatology workforce and care processes. METHODS: Pediatric rheumatologists and allied health professionals (AHPs) participated. A designee from each academic center provided workforce information including the number of providers, total and breakdown of full-time equivalents (FTEs), and triage processes. We calculated the clinical FTE (cFTE) available per 75,000 (recommended benchmark) and 300,000 (adjusted) children using 2019 census data. The national workforce deficit was calculated as the difference between current and expected cFTEs. Remaining respondents were asked about ambulatory practices. RESULTS: The response rate of survey A (workforce information) and survey B (ambulatory practice information) was 100% and 54%, respectively. The majority of rheumatologists (91%) practiced in academic centers. The median number of rheumatologists per center was 3 (IQR 3) and median cFTE was 1.9 (IQR 1.5). The median cFTE per 75,000 was 0.2 (IQR 0.3), with a national deficit of 80 cFTEs. With the adjusted benchmark, there was no national deficit, but there was a regional maldistribution of rheumatologists. All centers engaged in multidisciplinary practices with a median of 4 different AHPs, although the median FTE for AHPs was ≤ 1. Most centers (87%) utilized a centralized triage process. Of 9 (60%) centers that used an electronic triage process, 6 were able to calculate wait times. Most clinicians integrated quality improvement practices, such as previsit planning (67%), postvisit planning (68%), and periodic health outcome monitoring (36-59%). CONCLUSION: This study confirms a national deficit at the current recommended benchmark. Most rheumatologists work in multidisciplinary teams, but AHP support may be inadequate.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 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".