Putting the plain into pain language in English for Medical Purposes: Learner inquiry into patients’ online descriptive accounts
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
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.
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 | 0.004 | 0.013 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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