Adolescent medicine subspecialty workforce: Insights from Canada
Bibliographic record
Abstract
Objectives: Adolescent Medicine (AM) in Canada has undergone significant growth since being accredited by the Royal College of Physicians and Surgeons of Canada (RCPSC) in May 2007. A deeper understanding of the workforce is needed in order to identify current gaps, to improve clinical care and scholarly endeavors, and to inform future developments. Methods: This is the first AM workforce survey administered in Canada and included 39 multiple-choice and 3 open-ended questions. Descriptive statistics were calculated, and thematic analysis was used for open-ended questions. Results: We identified 62 AM specialists from across Canada. The overall response was 97% (60/62). Most AM specialists were women (39/53, 74%), Caucasian (38/53, 72%), between 30 and 39 years old (22/53, 42%), and completed their subspecialty training in either Toronto (24/48, 50%) or Montreal (12/48, 25%). Nearly half of participants worked in either the Toronto, Ontario (13/49, 27%) or Montreal, Quebec (10/49, 20%). Nearly all participants (46/49, 94%) practiced in large urban population centres and were based in academic health science centres. The primary clinical areas of focus included eating disorders (25/51, 49%) and mental health (9/51, 18%). Almost all participants were satisfied with their career choice (41/50, 82%). Two-thirds of the participants (31/48, 65%) believed that there was an insufficient number of AM specialists in Canada. Conclusions: Highlighting current characteristics of the AM subspecialty will help government and academic policymakers in understanding the workforce available to care for Canadian adolescents and the need to develop training programs and policies to address gaps and shortages.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".