The rhetoric of patient voice: Reported talk with patients in referral and consultation letters
Bibliographic record
Abstract
Referral and consultation letters are written to enable the exchange of patient information and facilitate the trajectory of patients through the healthcare system. Yet, these letters, written about yet apart from patients, also sustain and constrain professional relationships and influence attitudes towards patients. We analysed 35 optometry referral letters and 35 corresponding ophthalmology consultation letters for reported 'patient voice' coded as 'experience' or 'agenda' and we interviewed 15 letter writers (eight optometry students, six optometrists, and one community ophthalmologist). There were 80 instances of reported 'patient voice' in 35 letters. The majority (68%) of the instances occurred in referral letters, likely due to differences in both 'letter function' and 'professional stance.' Reported 'patient voice' occurred predominantly as 'experience' (81%) rather than 'agenda' instances. Letters writers focused on their readers' needs, thus a biomedical voice dominated the letters and instances of reported 'patient voice' were recontextualized for the professional audience. While reporting 'patient voice' was not the norm in these letters, its inclusion appeared to accomplish specific work: to persuade reader action, to question patient credibility, and to highlight patient agency. These letter strategies reflect professional attitudes about patients and their care.
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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 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".