Bibliographic record
Abstract
BACKGROUND: Prior studies have reported relatively low job satisfaction for general internists. We used data from a large US physician survey to assess correlates of satisfaction of general internists. METHODS: The Physician Worklife Survey was mailed to a national random stratified sample of 5704 US physicians. General internists were assessed for their satisfaction, training, patient mix, work hours, the likelihood of recommending their specialty to medical students, and job stability. We then compared them with a specialist sample (internal medicine subspecialists [IMSSs]) and a primary care sample (family physicians [FPs]). Logistic regression was used to model predictors of satisfaction, stress, and medical student recruitment. RESULTS: There were 2326 respondents (adjusted response rate, 52%): 450 (19%) were general internists; 502 (22%), FPs; and 438 (19%), IMSSs. General internists were less satisfied than were IMSSs with their relationships with colleagues and with patient care issues (P<.01 for both) and less satisfied than were FPs with community ties (P =.001). Global job, career, and specialty satisfaction were significantly lower for general internists vs FPs and IMSSs (P<.05). General internists spent proportionately more of their work week in the hospital than did FPs (20% vs 13%; P<.001) and more time providing outpatient care than did IMSSs (56% vs 42%; P<.001). General internists had more patients with complex medical and psychosocial problems than did FPs (P<.01) but fewer patients with complex medical problems than did IMSSs (P<.001). Higher satisfaction for general internists was associated with older physician age, less time pressure during office visits, fewer work hours, and fewer patients with complex psychosocial problems (P<.05 for all). General internists were less likely than were FPs to recommend their specialty to medical students (P<.001). Specialty satisfaction, female gender, and control of hassles predicted medical student recruitment by general internists. CONCLUSIONS: General internists' role of caring for patients with complex problems is associated with lower levels of satisfaction than for IMSSs and FPs. Adjusting caseload for patient complexity, expanding time for office visits, and additional training in the care of patients with psychosocially complex problems may improve the job satisfaction of general internists and medical student recruitment into the specialty.
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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 | 0.004 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".