Navigating the stages of an academic career for paediatricians
Bibliographic record
Abstract
In Canada, a major proportion of the country’s paediatricians work within the context of the paediatric academic health science centres (PAHSCs). These are either free-standing children’s hospitals or children’s units within general hospitals, all affiliated with a university faculty of medicine. In a recent work force analysis by the Paediatric Chairs of Canada (PCC), an association linking the 16 medical school departments of paediatrics, 1701 full-time and major part-time faculty members were identified, representing slightly more than one-half of the country’s paediatricians (1). The remaining paediatricians are increasingly involved in academic activities as more community hospitals become associated with expanding medical schools. Medical students intent on a career in paediatrics and, perhaps more importantly, paediatric residents deciding on their career paths, ought to be well informed about the options open to them, the appropriate training for each of these options as well as the expectations when entering their field of choice. Here, we address the issue of stages or phases of an academic career, and what to expect and how to successfully navigate the challenges of each phase. We make some assumptions, namely that the training for the position has been appropriate and that the job or career activity profile into which the candidate has been recruited matches this training. This is not to minimize either of these aspects of career planning and development; rather, they warrant attention in a separate article.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 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".