Growth curves: The experiences of Canadian paediatricians in their first 5 years of independent practice
Bibliographic record
Abstract
OBJECTIVES: Completing training is a rite of passage common to all physicians, yet our knowledge of the components in postgraduate paediatric education that equip learners for successful transition to practice is limited. In order to optimally design training programs, it is critical to develop a better sense of what early career paediatricians (ECPs) experience as they navigate this time of transition. METHODS: We created and distributed a 23-question survey via e-mail to 481 Canadian ECPs in September 2017, specifically to those who received Royal College certification in 2011 or later. Survey responses were obtained confidentially through an online platform (Survey Monkey). Descriptive statistics and thematic analysis were used to analyze responses to closed-ended and free text questions, respectively. RESULTS: Response rate was 42% with nearly 70% of the respondents self-identifying as general paediatricians. Factors facilitating transition to practice included: dedicated mentorship; supportive new colleagues and workplace environment; and ease of finding work. Identified challenges included: billing, finances, and practice management; adjusting to a different scope of practice and learning local resources; managing comfort level; and achieving work-life balance. Nearly half of the respondents expressed interest in mentoring new ECP colleagues. CONCLUSIONS: Our findings suggest that ECPs find clear value in mentorship, but desire further support to adapt to new practice contexts and activities. As a result, we must consider strategies in both individual programs and nationally that effectively prepare learners prior to transition and align with needs in the first years of independent practice.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".