Measuring recall of medical information in non‐English‐speaking people with cancer: A methodology
Bibliographic record
Abstract
BACKGROUND: Many patients who require an interpreter have difficulty remembering information from their medical consultations. Memory aids such as consultation audio-recordings may be of benefit to these patients. However, there is no established means of measuring patients' memory of medical information. OBJECTIVES: This study aimed to develop a method for eliciting and coding recall of medical information in non-English-speaking patients. DESIGN: This method, called Patient-Interpreter-Clinician coding (PICcode), was developed in the context of a phase II trial conducted in two outpatient oncology clinics in Melbourne, Australia, and was refined iteratively through consultation with an expert panel and piloting. Between-coder differences in early versions of the coding system were resolved through discussion and consensus resulting in refinements to PICcode. RESULTS: The final version of PICcode involved transcribing, translating and coding of audio-recorded consultations and semi-structured interviews (SSI). The SSIs were designed to elicit patients' free-recall of medical information. Every unit of medical information in the consultation was identified and categorized in a coding tree. SSIs were coded to identify the extent to which information was recalled from the consultation. DISCUSSION: The iterative changes involved in developing PICcode assisted in clarifying precise details of the process and produced a widely applicable coding system. PICcode is the most comprehensively described method of determining the amount of information that patients who use an interpreter recall from their medical consultations. PICcode can be adapted for English-speaking patients and other healthcare populations.
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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 | 0.046 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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 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".