Does digital health technology improve physicians’ job satisfaction and work–life balance? A cross-sectional national survey and regression analysis using an instrumental variable
Bibliographic record
Abstract
OBJECTIVES: To examine the association between physicians' use of digital health technology and their job satisfaction and work-life balance. DESIGN: A cross-sectional nationally representative survey of physicians and probit regression models were used to examine the association between using digital health technology and the probability of reporting high job satisfaction and a good work-life balance. Models included a rich set of covariates, including physicians' personality traits, and instrumental variable analysis was used to control for bias from unobservable confounders and reverse causality. SETTING: Clinical practice settings in Australia, including physicians working in primary care, hospitals, outpatient settings, and physicians working in the public and private sectors. PARTICIPANTS: Respondents to wave 11 (2018-2019) of the Medicine in Australia: Balancing Employment and Life (MABEL) longitudinal survey of doctors. The analysis sample included a broadly nationally representative sample of 7043 physicians, including general practitioners, specialists and physicians in training. PRIMARY AND SECONDARY OUTCOME MEASURES: The proportion of respondents who used any digital health technology; proportion answered 'moderately satisfied' or 'very satisfied' to the statement on job satisfaction: 'Taking everything into account, how do you feel about your work'; proportion agreeing or strongly agreeing to the statement on work-life balance: 'The balance between my personal and professional commitments is about right.' RESULTS: Physicians with positive beliefs about the effectiveness of using digital health technology were 3.8 percentage points (95% CI 2.7 to 5.0) more likely to use digital health technology compared with those who did not. Physicians with colleagues who already used digital health technology were also 4.1 percentage points (95% CI 2.6 to 5.6) more likely to use digital health technology. The availability of IT support and lack of privacy concerns increased the probability of using digital health technology by 1.6 percentage points (95% CI 1.0 to 2.3) and 0.5 percentage points (95% CI 0.1 to 1.0). Physicians who used digital health technology were 14.2 percentage points (95% CI -1.3 to 29.7) and 20.3 percentage points (95% CI 2.4 to 38.1) more likely to report respectively higher job satisfaction and good work-life balance, compared with the physicians who did not use it. CONCLUSIONS: Findings suggested digital health technology served more as a work resource than work demand for physicians who used it.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".