Bibliographic record
Abstract
In an article recently published in the Journal, Zelek and Phillips [1] attempt to identify some of the current context of the doctor – game as played at a Canadian hospital in January 2000. Some readers may appropriately ask What does this have to do with equity?, but personal experience suggests dysfunctional medical teamwork inevitably leads to diminishment in quality. The reasonable claim that lower quality medical care has a disproportionate effect on the less advantaged leads to the relevance of this topic to equity in health, in my opinion. The interpretations made by these authors appear reasonably self-evident. Anyone who has worked on a hospital ward in the United States or Canada will likely recognize in these vignettes strong similarities to personal observations and/or experiences. Res ipsa loquitur. But in matters this complex do gender and power represent the only two important variables? I can't help but think that temperament makes a large difference in physician – nurse interactions. Little has been published in the medical literature about this subject, but unpublished data indicate major differences among various medical careers on a test such as the Myers-Briggs. Do differences in career choices by temperament play a hidden role in studies such as the one by Zelek and Phillips? Most observers would likely agree that a comparison of neonatology, pediatrics and surgery would demonstrate great differences in temperament between each group, both at the nurse and physician level. During the late 20th century as the proportion of women among matriculating medical students increased markedly, the specialty selections of graduating physicians also shifted considerably, with women selecting areas such as pediatrics disproportionately. Is it possible that we have experienced a differential temperament shift as well? Could historical tensions between male physicians and female nurses be about temperament mismatches as much as about gender and power? Perhaps future research in this area will go beyond the traditional binary perspective and examine a more complex, but richer terrain.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".