When a physician marries a physician: effect of physician-physician marriages on professional activities.
Bibliographic record
Abstract
OBJECTIVE: To determine whether more Canadian family physicians are marrying other physicians and to examine the influence of physician-physician marriages on FPs' professional activities. DESIGN: Secondary analysis of a population survey (mailed questionnaire) using regression analysis. SETTING: Canadian family medicine. PARTICIPANTS: Family physicians who responded to the 2001 National Family Physician Workforce Survey conducted by the College of Family Physicians of Canada (CFPC) (13 088 respondents; 51.3% response rate). MAIN OUTCOME MEASURES: The main outcome measure was self-reported hours spent on professional activities during a typical week. Other information used in this analysis included marital status (currently married or not), hours spent providing dependent care each week, age (65 and older or younger than 65), sex, practice location (rural or not), and age of youngest child (younger than 6 or not). These factors were previously reported to affect amount of time spent on professional activities. Decade of graduation was used descriptively; having a physician spouse was considered a variable in the regression. RESULTS: About 86% of FPs were married; 16% of these were married to other physicians. The proportion of physician-physician marriages increased over time, mainly because male physicians increasingly married female physicians. The youngest cohort had the highest proportion of physician-physician marriages (20%). Having a physician spouse significantly reduced the hours devoted to professional practice. After taking into account the effects of other factors known to influence extent of professional activities, on average, female FPs in physician-physician marriages worked about 5 hours less weekly than other female physicians, and male FPs worked about 3 hours less weekly than other male physicians. The effect of other factors was consistent with previous reports of their influence on professional activities. CONCLUSION: These findings have implications for medical human resource planning. The cumulative effect on physicians' work time that physician-physician marriages have must be considered. Two physicians married to each other might be a marker for a couple with high discretionary income that allows them to make a lifestyle choice of reducing work time. If this is the case, reduction in professional activities is more likely to be found among FPs whose spouses can command a high salary.
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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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".