British doctors’ work–life balance and home-life satisfaction: a cross-sectional study
Bibliographic record
Abstract
PURPOSE: To assess British doctors' work-life balance, home-life satisfaction and associated barriers. STUDY DESIGN: We designed an online survey using Google Forms and distributed this via a closed social media group with 7031 members, exclusively run for British doctors. No identifiable data were collected and all respondents provided consent for their responses to be used anonymously. The questions covered demographic data followed by exploration of work-life balance and home-life satisfaction across a broad range of domains, including barriers thereto. Thematic analysis was performed for free-text responses. RESULTS: 417 doctors completed the survey (response rate: 6%, typical for online surveys). Only 26% reported a satisfactory work-life balance; 70% of all respondents reported their work negatively affected their relationships and 87% reported their work negatively affected their hobbies. A significant proportion of respondents reported delaying major life events due to their working patterns: 52% delaying buying a home, 40% delaying marriage and 64% delaying having children. Female doctors were most likely to enter less-than-full-time working or leave their specialty. Thematic analysis revealed seven key themes from free-text responses: unsocial working, rota issues, training issues, less-than-full-time working, location, leave and childcare. CONCLUSIONS: This study highlights the barriers to work-life balance and home-life satisfaction among British doctors, including strains on relationships and hobbies, leading to many doctors delaying certain milestones or opting to leave their training position altogether. It is imperative to address these issues to improve the well-being of British doctors and improve retention of the current workforce.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".