Promoting consultation recording practice in oncology: Identification of critical implementation factors and determination of patient benefit.
Bibliographic record
Abstract
155 Background: The objectives of this implementation study were to 1) identify and address the evidentiary, contextual, and facilitative mechanisms that serve to retard or promote the transfer and uptake of consultation recording use in oncology practice, and 2) follow patients during the first few days following receipt of the consultation recording to document, from the patient’s perspective, the benefits realized from listening to the recording. Methods: Nine medical and 9 radiation oncologists from cancer centers in three Canadian cities (Calgary, Vancouver, Winnipeg) recorded their primary treatment consultations for 228 patients newly diagnosed with prostate or breast cancer. The Digital Recording Use Semi-Structured Interview (DRUSSI) was conducted at two days post-consultation and at 1-week post-consultation. Each oncologist was given a feedback letter summarizing the consultation recording benefits reported by their patients. Results: Sixty-nine percent of patients listened to at least a portion of the recording within the first week following the consultation. Consultation recording favourableness ratings were high: 93.6% rated the intervention between 75–100 on a 100-point scale. Four main areas of benefit were reported: 1) Anxiety reduction; 2) Enhanced retention of information; 3) Better informed decision making; and 4) Improved communication with family members. Eight fundamental components of successful transfer and uptake of consultation recording practice were identified. Conclusions: Implementation research and additional randomized trials are needed to facilitate the transfer and uptake of consultation recording use so that far more patients and significant others may realize the associated benefits.
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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 | 0.032 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".