Culturally and linguistically diverse oncology patients' perspectives of consultation audio‐recordings and question prompt lists
Bibliographic record
Abstract
OBJECTIVE: Ethnicity and migrant status result in disparities with cancer burden and survival, with communication difficulties cited as the main barrier to access. Our research team tested a communication intervention package comprising consultation audio-recordings (ARs) and question prompt lists (QPLs) for low English-speaking (LES) patients with cancer. This study explored LES patient experiences, preferences, and recommendations regarding the communication package. METHODS: Participants completed a questionnaire and qualitative interview regarding ARs and QPLs. Eligibility criteria comprised aged ≥18 years old; a consultation with an oncologist between June 1, 2015 and April 1, 2016; an Arabic, Cantonese, Greek, or Mandarin professional interpreter booked for that consultation; and randomised to receive the communication intervention. RESULTS: Eighteen patients completed the qualitative interview and 17 completed the questionnaire. Fifteen reported listening to the AR at least once. Participants reported that QPLs and ARs provide support and assistance with remembering and understanding medical information. Both resources were seen as having applicability beyond the oncology setting in regards to improving health service delivery and continuity of care. However, patients felt that individual tailoring of the resources should be considered. Patients also found it useful to share ARs with family. CONCLUSIONS: The LES participants in this study considered the ARs and QPLs useful for most, but not all contexts. Recommendations regarding delivery and use highlight that these resources should be tailored and patient-driven. Further, patients foresaw a range of additional uses for consultation ARs within the broader healthcare context.
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How this classification was reachedexpand
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".