Are the career choices of paediatric residents meeting the needs of academic centres in Canada?
Bibliographic record
Abstract
BACKGROUND: Responsibility for training paediatric medical subspecialists in Canada lies primarily with the 16 academic paediatric departments. There has been no mechanism to assess whether the number of residents in training will meet the needs of currently vacant positions and/or the predicted vacancies to be created by anticipated faculty retirement in the next five years across the different paediatric medical subspecialties. HYPOTHESIS: At the present time, the training of the paediatric physician is not linked with the current and future needs of the academic centres where the vast majority of these paediatric subspecialists are employed. METHODS: The academic paediatric workforce database of the Paediatric Chairs of Canada (PCC) for the surveys obtained in 2009/2010 were analyzed. Data included the number of physicians working in each subspecialty, the number of physicians 60 years of age or older, as well as the number of residents and their level of training. RESULTS: There are some paediatric subspecialties in which the actual number of trainees exceeds the currently predicted need (eg, cardiology, critical care, hematology-oncology, nephrology, neurology, emergency medicine and genetic-metabolic). On the other hand, for other specialties (eg, adolescent medicine, developmental paediatrics, gastroenterology and neonatology), assuming there is no significant change to selection patterns, an important gap will persist or appear between the need and the available human resources. CONCLUSION: The present analysis was the first attempt to link the clinical orientation of trainees with the needs of the academic centres where the vast majority of these paediatric subspecialists work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".