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Record W4400453431 · doi:10.1136/bmjebm-2024-sdc.31

031 Just say ‘we don’t know’-shared decision-making in the face of uncertainty: an assessment of the sensibility of a model of shared decision making for children with medical complexity while hospitalised

2024· article· en· W4400453431 on OpenAlexaff
Francine Buchanan, Peter J. Gill, Sanjay Mahant, Naomi Gryfe Saperia, Sharon E. Straus, Christine Fahim, Karolyn Hardy Brown, Glyn Elwyn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsSt. Michael's HospitalInstitute for Clinical Evaluative Sciences
Fundersnot available
KeywordsSensibilityFace (sociological concept)Computer scienceMedical decision makingFace-to-faceInternet privacyMedicineSociologyEpistemologyPolitical science

Abstract

fetched live from OpenAlex

Introduction Children with medical complexity (CMC) are the most medically fragile sub-set of paediatric patients who require intensive support from caregivers.1 Due to the co-existence of underlying diseases, caregivers and clinicians of CMC face decisions with unclear answers, with inadequate evidence to support treatment options. A conceptual model of SDM for CMC2 addresses the process of SDM when faced with complex, ambiguous clinical situations in which all possible outcomes cannot be known. This poster will present a how a conceptual model of SDM for CMC was revised and assessed for content and face validity by end users. Methods Virtual focus groups were held with 5 health care providers and 11 caregivers of CMC via Zoom. A video describing the model of SDM for CMC, was presented followed by a series of open-ended questions surrounding the model’s look, language, and usability.3 Suggested changes to the model and attributes to maintain were discussed. The steering committee reviewed the proposed changes which were then approved by consensus. In collaboration with a graphic artist, A revised model was produced with a graphic artist. The revised model was distributed to focus group participants with a survey to appraise sensibility using a 5 point likert scale. Results Mean score visual acceptability was 4.1 with 4.2 for usability. Discussion/Conclusion The changes to the models addressed the need for better reflect the decision maker as a person, the goal of relationship building and continuous nature of knowledge building. The revised model was well received by respondents regarding layout, content, language and usability. References Cohen E, Kuo DZ, Agrawal R, Berry JG, Bhagat SKM, Simon TD, et al. Children with medical complexity: an emerging population for clinical and research initiatives. Pediatrics. 2011;127(3):529–38. Buchanan FE. Making difficult decisions: an activity-theory informed qualitative study of shared decision- making for children with medical complexity [Thesis]. 2021. Légaré F, Stacey D, Pouliot S, Gauvin FP, Desroches S, Kryworuchko J, et al. interprofessionalism and shared decision-making in primary care: a stepwise approach towards a new model. J Interprof Care. 2011;25(1):18–25.

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)0.041
metaresearch head score (Gemma)0.118
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.218

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.118
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.001
Science and technology studies0.0030.011
Scholarly communication0.0110.006
Open science0.0020.009
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.237
GPT teacher head0.491
Teacher spread0.254 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes1
Has abstractyes

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