An advance care plan decision support video before major surgery: a patient- and family-centred approach
Bibliographic record
Abstract
Objective Video-based advanc care planning (ACP) tools have been studied in varied medical contexts; however, none have been developed for patients undergoing major surgery. Using a patient- and family-centredness approach, our objective was to implement human-centred design (HCD) to develop an ACP decision support video for patients and their family members when preparing for major surgery. Design and methods The study investigators partnered with surgical patients and their family members, surgeons and other health professionals to design an ACP decision support video using key HCD principles. Adapting Maguire’s HCD stages from computer science to the surgical context, while also incorporating Elwyn et al ’s specifications for patient-oriented decision support tool development, we used a six-stage HCD process to develop the video: (1) plan HCD process; (2) specify where video will be used; (3) specify user and organisational requirements; (4) produce and test prototypes; (5) carry out user-based assessment; (6) field test with end users. Results Over 450 stakeholders were engaged in the development process contributing to setting objectives, applying for funding, providing feedback on the storyboard and iterations of the decision tool video. Throughout the HCD process, stakeholders’ opinions were compiled and conflicting approaches negotiated resulting in a tool that addressed stakeholders’ concerns. Conclusions Our patient- and family-centred approach using HCD facilitated discussion and the ability to elicit and balance sometimes competing viewpoints. The early engagement of users and stakeholders throughout the development process may help to ensure tools address the stated needs of these individuals. Trial registration number NCT02489799.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".