Education for communication: much already known, so much more to understand
Bibliographic record
Abstract
Education for communication: much already known, so much more to understandThe need for formal communication skills teaching and learning in health care education is well established.1 A substantial body of evidence has also accumulated about the problems of communication between health care professionals and patients and the bene®ts of good communication. 2 There is also an extensive literature about `how to do it', 3 although much of this is descriptive.More evidence about the `how?', the `what?', the `when?' is to be welcomed.In this themed issue there are six papers looking speci®cally at teaching and learning communication.Each has intrinsic interest, but a number of themes also run through them, including the need for training and support for teachers, the timing of teaching and learning and the challenges of researching these areas.
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 | 0.025 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.023 | 0.047 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.017 | 0.025 |
| Insufficient payload (model declined to judge) | 0.039 | 0.010 |
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".