Expert and trainee determinations of rhetorical relevance in referral and consultation letters
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.
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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 | 0.028 | 0.196 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".