Primary care physician digital health profile and burnout: an international cross-sectional study.
Bibliographic record
Abstract
Digital health offers promising solutions for enhancing patient care, yet adoption varies among physicians, partly due to concerns about administrative burdens and burnout. This study assessed digital health use and burnout among primary care physicians in 10 OECD countries and examined their relationship. Methods: We conducted a secondary analysis of "The Commonwealth Fund's 2022 International Health Policy Survey," including 9526 randomly selected primary care physicians (general practitioners or pediatricians in ambulatory care) from 10 OECD countries. We created a digital health score based on the use and frequency of digital tools. Self-reported burnout and related outcomes were analyzed. Cross-country differences were assessed using stratified analyses. Associations between digital health and burnout and related outcomes were explored using stratified analyses and logistic regressions. Results: Most physicians used electronic records; video consultations or connected tools for chronic care. Digital health scores were highest in the Netherlands and UK, and lowest in Germany and Switzerland. 35% of physicians reported burnout, with the highest prevalence in New Zealand (49%) and Canada (46%), and lowest in the Netherlands (12%) and Switzerland (18%). Digital health use positively correlated with workload dissatisfaction but not with burnout, stress, satisfaction with administrative work, or work-life balance. Conclusion: Physicians' digital health use and burnout varied substantially across countries but were not correlated. While digital health is often considered a factor linked to physician burnout, our results do not support this view. They also highlight the need to ensure that digital health reduces, rather than exacerbates, physicians' workload.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".