Barriers and facilitators to the implementation of audio-recordings and question prompt lists in cancer care consultations: A qualitative study
Bibliographic record
Abstract
OBJECTIVE: Question prompt lists (QPLs) and consultation audio-recordings (CARs) are two communication strategies that can assist cancer patients in understanding and recalling information. We aimed to explore clinician and organisational barriers and facilitators to implementing QPLs and CARs into usual care. METHODS: Semi-structured interviews with twenty clinicians and senior hospital administrators, recruited from four hospitals. Interviews were recorded, transcribed verbatim and thematic descriptive analysis was utilised. RESULTS: CARs and QPLs are to some degree already being initiated by patients but not embedded in usual care. Systematic use should be driven by patient preference. Successful implementation will depend on minimal burden to clinical environments and feedback about patient use. CARs concerns included: medico-legal issues, ability of the CAR to be shared beyond the consultation, and recording and storage logistics within existing medical record systems. QPLs issues included: applicability of the QPLs, ensuring patients who might benefit from QPL's are able to access them, and limited use when there are other existing communication strategies. CONCLUSIONS: While CARs and QPLs are beneficial for patients, there are important individual, system and medico-legal considerations regarding usual care. PRACTICE IMPLICATIONS: Identifying and addressing practical implications of CARs and QPLs prior to clinical implementation is essential.
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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 | 0.025 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".