Bibliographic record
Abstract
When I began lecturing on marriage to medical students and physicians about 25 years ago, I used a cartoon to introduce my lectures. The cartoon showed 2 physicians having lunch together in the hospital. The caption says: “Show me a doctor whose wife is happy, and I'll show you a man who's neglecting his practice.” Table 1 Expectations that people bring to a relationship Hopes Needs (conscious and unconscious) Social values Family expectations Economics Religion Ethnicity Fit of the 2 individuals View it in a separate window Fast-forward now to the 21th century and consider the changed demographics: 50% of physicians are women, lots of spouses are men, many physicians do not have wives or husbands but “partners” (who may be the opposite or same sex), and few physicians have or make the time to eat together in the hospital cafeteria. But, with some gender-neutral modernizing, isn't the caption still apt? Aren't physicians still torn between their calling—the needs of their patients—and the needs of their families? Don't some physicians still think that their patients come first and that their spouses or partners must simply understand? In this article, I attempt to answer 4 questions: What do we know about healthy, intimate relationships? What are some of the unique challenges to a relationship posed by a career in medicine? What is the effect of a healthy relationship on physician well-being? What are some strategies to create and maintain relationship intimacy?
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".