Time Study of Clinical and Nonclinical Workload in Pathology and Laboratory Medicine
Bibliographic record
Abstract
We describe a detailed, cross-sectional, self-report time study of laboratory physician tasks in a regionalized, multisite academic setting, using custom data collection templates programmed into personal digital assistants (PDAs). The 7-week study was completed by 56 medical and scientific staff (86% participation rate). Participants recorded 12,781 PDA entries of specific tasks completed during the study period. The mean number of entries per worked day per participant was 8.14 (range, 1.96-14.33). Study results demonstrated that professional staff worked, on average, 53.5 hours per week. Percentage work time spent in each activity area was as follows: clinical, direct, 50.6%; administration, 18.5%; clinical, indirect, 9.5%; research, 8.2%; learning/continuing education, 5.3%; teaching, 4.9%; and quality assurance, 3.1%. These percentages varied significantly by laboratory medicine subspecialty and by type of academic appointment. The findings confirm that activities not directly involved with patient care, such as administration, quality assurance, teaching, research, and professional development, typically occupy 40% to 50% of a laboratory physician's time.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".