The generation and gender shifts in medicine: an exploratory survey of internal medicine physicians
Bibliographic record
Abstract
BACKGROUND: Two striking demographic shifts evident in today's workforce are also apparent in the medical profession. One is the entry of a new generation of physicians, Gen Xers, and the other is the influx of women. Both shifts are argued to have significant implications for recruitment and retention because of assumptions regarding the younger generation's and women's attitudes towards work and patient care. This paper explores two questions regarding the generations: (1) How do Baby Boomer and Generation X physicians perceive the generation shift in work attitudes and behaviours? and (2) Do Baby Boomer and Generation X physicians differ significantly in their work hours and work attitudes regarding patient care and life balance? Gen Xers include those born between 1965 and 1980; Baby Boomers are those born between 1945 and 1964. We also ask: Do female and male Generation X physicians differ significantly in their work hours and work attitudes regarding patient care and life balance? METHODS: We conducted exploratory interviews with 54 physicians and residents from the Department of Medicine (response rate 91%) and asked about their perceptions regarding the generation and gender shifts in medicine. We limit the analyses to interview responses of 34 Baby Boomers and 18 Generation Xers. We also sent questionnaires to Department members (response rate 66%), and this analysis is limited to 87 Baby Boomers' and 65 Generation Xers' responses. RESULTS: The qualitative interview data suggest significant generation and gender shifts in physicians' attitudes. Baby Boomers generally view Gen Xer physicians as less committed to their medical careers. The quantitative questionnaire data suggest that there are few significant differences in the generations' and genders' reports of work-life balance, work hours and attitudes towards patient care. CONCLUSION: A combined qualitative and quantitative approach to the generation shift and gender shift in medicine is helpful in revealing that the widely held assumptions are not necessarily reflective of any significant differences in actual work attitudes or behaviours of Boomer and Gen X physicians or of the younger generation of women entering medicine.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".