Political arithmetick : physician productivity in concept and measurement : draft discussion paper
Bibliographic record
Abstract
This paper was written for the Fourth Annual Medical Workforce Conference, San Francisco, November 4-7, 1999. The organizers intent was to have the conference papers published, and to have the discussants for the papers in each session contribute a synthesis paper for that session. The papers synthesized here are: Harding, John, and Warwick Conn, 'Workforce Productivity in the Australian Medical workforce', Watanabe, Mamoru, Lynda Buske and Jill Strachan, 'Canadian Physician Workforce Productivity', Maynard, Alan and Karen Bloor, 'Workforce Productivity in the U.K. NHS: Measurement, Variation and Incentives'. Unfortunately the organizers plans for publication did not materialize. This paper reached the antepenultimate stage of being circulated to the session participants for comment and possible revisions, after which references were to have been added. But the overall project was abandoned before any comments were received, and the paper was never completed for publication. The broader issue of physician productivity has, however, re-emerged in Canada. I believe that the paper offers a useful analytic framework for addressing that topic along with fairly detailed illustrative examples of its application to the descriptive material, institutional and statistical, provided in the session papers. Much has changed since; no attempt has been made to up-date those papers. But the process of applying the analytic framework to the world as it then was (said to be) provides, I think, a clear guide for a similar application to present circumstances.
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 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 | 0.087 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.042 | 0.016 |
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".