A real-time measurement of general practice workload in the Republic of Ireland: a prospective study
Bibliographic record
Abstract
BACKGROUND: Demand for GP services in the Republic of Ireland (RoI) is increasing, and the resultant escalation in workload demands is an issue of growing concern. Accordingly, the accurate measurement and description of GP workload is essential to inform future healthcare planning. AIM: To provide a real-time measurement of GP workload with respect to hours worked and of proportional time expenditure on typical workload activities. DESIGN AND SETTING: A prospective study among GPs in the RoI that took place from January 2019 to March 2019. METHOD: Participants were invited to enrol in the study by direct email invitation and via notifications posted within GP-specific monthly journals; online forums; and a social media platform. Participants used a time-management software program to self-record workload activity in real time over 6 weeks. RESULTS: In total, 123 GPs were included for final analyses with a total of 8930 hours of activity recorded. The mean duration of a two-session day (excluding break-time) was 9.9 hours (95% confidence interval [CI] = 9.7 to 10.0; interquartile range [IQR] 7.9 to 13.9). Of this time, 64% was spent on clinical consultations. In total, 25.4% of activity was recorded outside the hours of 9.00 am and 5.00 pm. An average of 12.4 face-to-face consultations were completed per session of activity. The mean duration of a 10-session week was greatest for the partner (50.8 hours; 95% CI = 49.8 to 51.9) and >55-year-old (50.8 hours; 95% CI = 49.3 to 52.2) demographics, relative to their respective colleagues. CONCLUSION: To the authors' knowledge, this is the first study to provide an objective, accurate, and granular real-time measurement of GP workload in the RoI, demonstrating the significant volume and variety of work undertaken by GPs in the RoI.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".