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Record W2749570738 · doi:10.1089/jpm.2017.0106

Developing the Storyline for an Advance Care Planning Video for Surgery Patients: Patient-Centered Outcomes Research Engagement from Stakeholder Summit to State Fair

2017· article· en· W2749570738 on OpenAlexfundno aff
Rebecca A. Aslakson, Anne Schuster, Thomas J. Lynch, Matthew J. Weiss, Lydia Gregg, Judith Miller, Sarina R. Isenberg, Norah L. Crossnohere, Alison M. Conca-Cheng, Angelo E. Volandes, Thomas J. Smith, John F. P. Bridges

Bibliographic record

VenueJournal of Palliative Medicine · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchPatient-Centered Outcomes Research Institute
KeywordsSummitStakeholderMedicineStakeholder engagementMedical educationAdvance care planningStoryboardNursingPalliative carePublic relationsMultimediaComputer science

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.056
metaresearch head score (Gemma)0.103
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.296

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.103
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.003
Scholarly communication0.0050.004
Open science0.0020.013
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.735
GPT teacher head0.581
Teacher spread0.154 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2017
Admission routes1
Has abstractyes

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