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Record W2967357427 · doi:10.20381/ruor-22436

Implementation of Shared Decision Making in Pediatric Clinical Practice

2018· dissertation· en· W2967357427 on OpenAlexfundaboutno aff
Laura Boland

Bibliographic record

VenueuO Research (University of Ottawa) · 2018
Typedissertation
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchUniversity of Ottawa
KeywordsClinical PracticeMedicineComputer scienceMedical educationFamily medicine

Abstract

fetched live from OpenAlex

Shared decision making (SDM) is rarely used in pediatric clinical practice. The purpose of this dissertation was to explore factors influencing SDM implementation in pediatric clinical practice. We conducted three studies that were guided by the Ottawa Model of Research Use (OMRU): Study 1 was a systematic review using Cochrane methods and the Mixed Methods Appraisal Tool to determine pediatric SDM barriers and facilitators from multiple perspectives. Eighty studies, of low to high quality, were included. At each OMRU level, frequently cited barriers were: option features (decision), poor quality information (innovation), emotional state (adopter), power relations (relational), and insufficient time (environment). Frequently cited facilitators were: lower stake decisions (decision), agreement with SDM (adopter), high quality information (innovation), trust and respect (relational), and SDM tools/resources (environment). Across participant types, frequently cited barriers were: insufficient time (healthcare providers (HCP)), option features (parents), power imbalances (children), and HCPs’ SDM skills (observers). Frequently cited facilitators were: good quality information (HCPs) and agreement with SDM (parents/children). Study 2 was a post-test design that evaluated SDM knowledge and acceptability of learners who completed the Ottawa Decision Support Tutorial (ODST). Most learners were HCPs (62%). Overall, ODST learners had a median knowledge test score of 8/10 (IQR = 7-9; n=6604) and 90% reported good or excellent impressions (n=4276) after completing the tutorial. Few learners suggested improvements. Study 3 used mixed methods to evaluate pediatric HCPs’ perceived SDM barriers and facilitators after training (ODST plus workshop). Participants completed a SDM barrier survey (n=60; 88% response rate) and semi-structured interview (n=11). Their intention to use SDM was high (mean score = 5.6/7, SD=0.8). However, 90% of respondents reported minimal SDM use after training. Main barriers were lack of buy-in (adopter level) and time constraints (environmental level). Healthcare providers wanted a team-based approach to SDM training (training level). Adopters face numerous and diverse barriers to SDM use, before and after SDM training. Pediatric HCPs who completed the ODST were knowledgeable about SDM. Despite positive intentions, training alone was insufficient to achieve routine SDM use. These findings can inform intervention development to promote SDM implementation in pediatric clinical practice.

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)0.141
metaresearch head score (Gemma)0.329
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.743

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1410.329
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.003
Science and technology studies0.0020.005
Scholarly communication0.0070.006
Open science0.0030.009
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.416
GPT teacher head0.610
Teacher spread0.194 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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