The Training and Career Paths of Canadian Paediatric Residents, 2004–2010
Bibliographic record
Abstract
Canadian pediatric residents usually pursue training in general pediatrics or subspecialty pediatrics. Graduates can work in community-based settings, hospital-based settings, or a combination thereof. Furthermore, new graduates may work in large urban or rural/remote centers. To date, no study has profiled the training and career paths of Canadian pediatric residents.The goal of this study was to profile the training and career paths of pediatric residency program graduates in Canada.A survey of all pediatric residency programs was completed in 2010 and updated in 2011. All residency programs in Canada participated. Data on residents were reported by their core training program (at completion of their third year of postgraduate training).A total of 699 residents completed their core training in pediatrics in Canada between 2004 and 2010. The annual number of pediatric residents who completed their core training rose from 83 in 2004 to 122 in 2010. Training path data were available for 685 (98%) residents. Overall, 430 (63%) residents completed subspecialty training, whereas 255 (37%) completed general pediatric training only. There was a significant increase in the frequency of subspecialty training from the early graduates (2004–2007) to the later graduates (2008–2010) (Fig 1). Career path data were available for 665 (95%) of all Canadian pediatric residents: 405 (61%) of residents were working as subspecialists or still in training, 245 were working as general pediatricians, and 15 graduates (2%) had other practice types. Of all residents currently in practice, only 36 (8%) were working in rural/remote or underserviced areas.More than 60% of all Canadian pediatric residents pursue subspecialty careers. There was a significant increase in the frequency of subspecialty training among later-year graduates. Few graduates are practicing in rural/remote or underserviced areas. Canadian pediatric residency programs may not be producing the right mix of graduates to meet societal needs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".