Putting the plain into pain language in English for Medical Purposes: Learner inquiry into patients’ online descriptive accounts
Bibliographic record
Abstract
Abstract The need to teach medical students plain language for their future engagement in pain communication can no longer be underestimated. Pain education has traditionally neglected the teaching of pain language, yet patients’ descriptive accounts have been acknowledged as the standard in medical care. English for Medical Purposes (EMP) can make its contribution to tertiary pain education, especially at a time when the plain language paradigm is considered key for health literacy. This is not to say that teaching specialized language and plain language for specific purposes are mutually exclusive. Yet, developing EMP learners’ understanding of the use of authentic plain pain language is also crucial for their future professional practice. This study reports on a pedagogical experiment conducted with the aim of enhancing EMP learners’ understanding of the lexico-grammatical features of pain language in patients’ descriptive accounts and in the use of pain assessment tools. The experiment was framed by the Hallidayan lexico-grammatical model of pain. Following a data-driven learning approach, students compiled a small DIY corpus of accounts from online health support groups and exploited its direct use through corpus-based tasks. These were designed to facilitate learners’ understanding of the features of pain language and of patients’ use of pain descriptors related to those in the McGill Pain assessment tool currently employed in medical care. Learners further broadened their understanding of pain language in other contexts of use while taking notes to fulfil the designed tasks. These helped shed light on the pedagogical practice here proposed.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".