Preresidency publication record and its association with publishing during paediatric residency
Bibliographic record
Abstract
OBJECTIVE: To determine whether an association exists between the publication of journal articles before and during paediatrics residency. METHODS: A retrospective search of PubMed was conducted for publications by all 567 Canadian paediatricians certified between 2009 and 2012, inclusive. Paediatricians were separated into groups based on the number of articles published preresidency (0 or ≥1) and during residency (0 or ≥1). The methodology was validated using a group of local paediatricians who were contacted to verify whether their publications were identified accurately. RESULTS: A total of 160 of 567 (28%) certified paediatricians had preresidency publications; of these, 93 (58%) subsequently published during their residency period. Among the remaining 407 (72%) paediatricians without preresidency publications, 129 (32%) published during residency. The association between publication before and during paediatric residency was statistically significant (OR 2.98 [95% CI 2.04 to 4.36]; P<0.001). Results from the validation analysis suggested the methodology correctly identified pre- and during residency publication status with 87% and 90% accuracy, respectively. CONCLUSION: Individuals with previous publications were more likely to publish as residents; however, 42% of individuals with pre-residency publications did not publish as residents. Residency selection committees may find these data helpful in assessing the publication potential of their applicants. In addition, this information may assist in building more targeted and individualized research curricula within residency programs.
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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 | 0.033 | 0.252 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".