Canadian-based case studies of PA models of care (Commentaries on health services research)
Bibliographic record
Abstract
The authors used a time and motion study (office hours) and self-reported diary (after hours) to describe how time is spent in ambulatory practice. Family medicine, internal medicine, cardiology, and orthopedic physicians were observed. In-office physicians spent 27% of their total time on direct clinical face time with patients and 49% of their time on electronic health records (EHRs) and deskwork. While in the examination room with patients, they spent 53% of the time on direct clinical face time and 37% on EHRs and deskwork. Physicians reported 1 to 2 hours of after-hours work each night, devoted mostly to EHR tasks. For every hour physicians provide direct clinical face time to patients, nearly 2 additional hours is spent on EHRs and deskwork in the clinic day. Outside office hours, physicians spend another 1 to 2 hours of personal time each night doing additional computer and other clerical work.1 Commentary by Roderick S. Hooker: A time and motion study is a business efficiency technique combining the time study work of Frederick Taylor with the motion study work of Frank and Lillian Gilbreth a century ago. This technique remains applicable today in human resource and animal observation investigations.2 Additionally, time-motion data are typically considered highly valued evidence in efficiency research. Jane Record's seminal time-motion study on PAs and supervising physicians in the 1970s remains embedded in medical labor economics as early evidence of team-based synergy.3 What this American Medical Association-sponsored physician time-motion study illustrates is that EHRs are culprits in offsetting patient time with clerical work that consumes twice as much labor. EHRs were not present in early organizational studies of physicians and other health professionals but have now emerged to be the bane of existence for many fatigued providers. This outpatient study validates inpatient observations that what providers perceive they are doing daily differs significantly from what they are systematically observed to be doing.4
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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 | 0.021 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".