O-46 Advanced cancer patients’ perspectives on a video decision support aid used to enhance goals of care discussions
Bibliographic record
Abstract
Background Advance Care Planning/Goals of Care discussions can positively impact the quality of end-of-life care but few studies have evaluated Goals of Care video decision support aids in the setting of advanced cancer. Aim To explore perspectives of patients with advanced cancer on the use of a video decision support aid developed to enhance awareness of different Goals of Care (GOC) and to examine how the video influences participants’ knowledge and perceptions about GOC. Methods A pre-post design using a qualitative approach was used. Before and after watching the video, semi-structured interviews were conducted with 14 participants from an Outpatient Radiation Oncology Bone Metastases Clinic. Extended Elaboration Likelihood Model was the conceptual framework used to guide data analysis. Results Participants found the video scenarios made the GOC framework more personally relevant. They were able to relate their experiences to the video. After watching the video, nine participants identified their current approach to care as either medical care or comfort care. Participants found the timing and professional clinic environment appropriate for watching the video. Several participants expressed their intention to initiate discussions with their health care providers and to formalise their GOC plans. Discussion Ability to relate past and present experiences to the scenarios shown in the video influenced effectiveness of the video. Video made the GOC framework “real” and helped participants articulate personalised questions for their health care providers. Conclusion A purpose-specific video was a useful tool to engage patients in GOC in an outpatient oncology clinic.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".