Developing the Storyline for an Advance Care Planning Video for Surgery Patients: Patient-Centered Outcomes Research Engagement from Stakeholder Summit to State Fair
Bibliographic record
Abstract
BACKGROUND: Patient-centered outcomes research (PCOR) methods and social learning theory (SLT) require intensive interaction between researchers and stakeholders. Advance care planning (ACP) is valuable before major surgery, but a systematic review found no extant perioperative ACP tools. Consequently, PCOR methods and SLT can inform the development of an ACP educational video for patients and families preparing for major surgery. OBJECTIVE: The objective is to develop and test acceptability of an ACP video storyline. DESIGN: The design is a stakeholder-guided development of the ACP video storyline. Design-thinking methods explored and prioritized stakeholder perspectives. Patients and family members evaluated storyboards containing the proposed storyline. SETTING/SUBJECTS: The study was conducted at hospital outpatient surgical clinics, in-person stakeholder summit, and the 2014 Maryland State Fair. MEASUREMENTS: Measurements are done through stakeholder engagement and deidentified survey. RESULTS: Stakeholders evaluated and prioritized evidence from an environmental scan. A surgeon, family member, and palliative care physician team iteratively developed a script featuring 12 core themes and worked with a medical graphic designer to translate the script into storyboards. For 10 days, 359 attendees of the 2014 Maryland State Fair evaluated the storyboards and 87% noted that they would be "very comfortable" or "comfortable" seeing the storyboard before major surgery, 89% considered the storyboards "very helpful" or "helpful," and 89% would "definitely recommend" or "recommend" this story to others preparing for major surgery. CONCLUSIONS: Through an iterative process utilizing diverse PCOR engagement methods and informed by SLT, storyboards were developed for an ACP video. Field testing revealed the storyline to be highly meaningful for surgery patients and family members.
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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 | 0.056 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".