Life before and after residents: subjective reports on quality of life from urologists since inception of a new residency program
Bibliographic record
Abstract
Background: It is difficult to determine the effect of a residencyprogram on the life of staff urologists. The objective of this studywas to obtain subjective reports from urologists who have practicedbefore and after the implementation of a training program on howit affects their careers in 5 spheres: education, job-stress, free time,financial life and subjective quality of life.Methods: We asked urologists from McMaster University to completea questionnaire to quantify how their current experienceshave changed compared to the pre-residency program era on abalanced 7-point scale (4 = neutral).Results: The response rate was 100% (9/9). Eight of the 9 urologists(89%) reported they would implement the program againif they could rewind the clock. Eight of 9 reported their overallcareer-related quality of life improved, with an average rating of5.1 on the 7-point scale. The quality of continuing education wasthe most positive ranking at 5.4 followed by job stress at 5.2. Theoutcomes measured below 4 (neutral) were earning potential at3.8 and ability to engage in pastimes at 3.4. Earning potential wasclustered tightly around neutral, with 7 of the 9 respondents reportingno change. The largest standard deviation, corresponding tothe most disagreement, was in their ability to engage in pastimes.Conclusion: Even with a mild decrease in earning potential andincreased job stress, McMaster urologists feel their quality of lifeand continuing education have improved since the program’simplementation; these urologists are almost uniformly happy theystarted a residency teaching program at their centre.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".