Longitudinal Assessment of the Timing of Career Choice Among Pediatric Residents
Bibliographic record
Abstract
OBJECTIVE: To determine the timing of and key factors in resident decision making to pursue either a career in general pediatrics or subspecialty training. DESIGN: We used a 10-item fixed-choice questionnaire that focused on exploring how and when pediatric residents make career choices. SETTING: The survey was administered to all categorical pediatric residents in the United States and Canada as part of the General Pediatrics In-Training Examination in 2007 and 2009. The 2007 level 1 residents and 2009 level 3 residents were matched by a unique person identifier to create a longitudinal data set. PARTICIPANTS: A total of 2305 individuals completed the survey as level 1 residents in 2007 and level 3 residents in 2009, representing a retention rate of 83.5%. MAIN OUTCOME MEASURES: Change in individual and aggregate pediatric resident response over time. RESULTS: A similar number of individuals planned to pursue fellowship training in 2007 and 2009 (1026 vs 1062). Among this group, 745 (72.6%) of the 2009 residents were the same individuals who had indicated that they planned to pursue fellowship training in 2007. A total of 258 (71.9%) of all residents who reported in 2007 that they intended to pursue careers in general pediatrics with little or no inpatient care were still planning to do so in 2009. CONCLUSIONS: Most pediatricians make their decisions regarding pursuit of a career in primary care or to complete a fellowship before they ever enter residency training. It is unknown whether a similar timeline of decision making is consistent across specialties.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".