Crossing Between Clinical Work and Scholarly Publishing: Early-Career Neurosurgeons as Clinician-Researchers
Bibliographic record
Abstract
Medical professionals who assume multiple roles as clinicians and researchers are commonly found in hospitals worldwide. Likewise, in many Chinese hospitals, particularly among early-career medical professionals with MD and PhD degrees, active engagement in both clinical practice and scientific research is expected. In the current ethnographic study, the cultural-historical activity theory is used to explore how three early-career neurosurgeons at a tier-one northern Chinese hospital conduct and write research for publishing in Science Citation Index (SCI)-indexed journals in addition to handling their heavy clinical workload. Drawing on multiple sources of qualitative data, including semi-structured interviews, two-week naturalistic observations, field notes, photographs, and daily activity logs with three neurosurgeons, the study findings highlight Chinese neurosurgeons conduct and write up their research using patient data and collaborating with laboratories while seeking academic language editing and peer feedback. Ethical considerations of clinician-researchers’ scholarly publishing process, implications for researching medical professionals’ boundary crossings, and future research directions are also discussed.
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 | 0.033 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.031 | 0.028 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".