Expert and trainee determinations of rhetorical relevance in referral and consultation letters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Referral and consultation letters ferry patients among providers, negotiating co-operative care. Our study examined how "relevance" is signalled and decoded in these letters, from the perspective of both experts and trainees in three clinical specialties. METHODS: 104 letters were collected from 16 physicians representing family medicine, psychiatry and surgery. Interviews were conducted with 14 of these physicians and 13 residents from the three specialties. All documents and transcripts were analysed for emergent themes. RESULTS: Six rhetorical factors influenced expert physicians' decisions about what material is relevant: educational, professional, audience, system-institutional, medical-legal, and evaluative. Each specialty placed different emphasis on these factors. Trainees reported having no instruction regarding how to construct rhetorically relevant letters, and they demonstrated awareness of only three of the factors identified by experts--professional, audience and evaluative. Experts and trainees differed in their understanding and application of these three factors. CONCLUSIONS: This research demonstrates that six rhetorical factors influence relevance decisions in letter writing, and that experts address these factors in tacit, dynamic and discipline-specific ways. Trainees share with experts an appreciation of the rhetorical functions of referral and consultation letters, but lack a comprehensive understanding of the influential factors and do not receive instruction in them. These findings provide a framework for instruction in this domain to equip novices to meet the expectations of their professional audiences successfully.
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it