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Record W1713926677 · doi:10.2196/resprot.4591

Evaluating Comparative Effectiveness Research Priorities for Care Coordination in Chronic Obstructive Pulmonary Disease: A Community-Based eDelphi Study

2015· article· en· W1713926677 on OpenAlexvenueno aff
Michael Stellefson, Julia M. Alber, Samantha R. Paige, Daniela Castro, Briana Singh

Bibliographic record

VenueJMIR Research Protocols · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Center for Health StatisticsCenters for Disease Control and Prevention
KeywordsStakeholderLikert scaleMedical educationComparative effectiveness researchMedicineData collectionFamily medicinePsychologyNursingAlternative medicinePublic relationsSociologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Despite research supporting the use of care coordination in chronic obstructive pulmonary disease (COPD), there is relatively little known about the comparative effectiveness of different strategies used to organize care for patients. To investigate the most important COPD care coordination strategies, community-based stakeholder input is needed, especially from medically underserved populations. Web-based platforms are electronic tools now being used to bring together individuals from underrepresented populations to share input and obtain clarification on comparative effectiveness research (CER) ideas, questions, and hypotheses. OBJECTIVE: Use low computer-literate, collaborative survey technology to evaluate stakeholder priorities for CER in COPD care coordination. METHODS: A mixed-method, concurrent triangulation design was used to collect survey data from a virtual advisory board of community-based stakeholders including medically underserved patients with COPD, informal caregivers, clinicians, and research scientists. The eDelphi method was used to conduct 3 iterative rounds of Web-based surveys. In the first 2 survey rounds, panelists viewed a series of "mini research prospectus" YouTube video presentations and rated their level of agreement with the importance of 10 COPD care coordination topics using 7-point Likert scales. In the final third-round survey, panelists ranked (1=most important, 8=least important) and commented on 8 remaining topics that panelists favored most throughout the first 2 survey rounds. Following the third-round survey, panelists were asked to provide feedback on the potential impact of a Web-based stakeholder engagement network dedicated to improving CER in COPD. RESULTS: Thirty-seven panelists rated the following care coordination topics as most important (lower means indicate greater importance): (1) measurement of quality of care (mean 2.73, SD 1.95); (2) management of COPD with other chronic health issues (mean 2.92, SD 1.67); (3) pulmonary rehabilitation as a model for care (mean 3.72; SD 1.93); (4) quality of care coordination (mean 4.12, SD 2.41); and (5) comprehensive COPD patient education (mean 4.27, SD 2.38). Stakeholder comments on the relative importance of these care coordination topics primarily addressed the importance of comparing strategies for COPD symptom management and evaluating new methods for patient-provider communication. Approximately one half of the virtual panel assembled indicated that a Web-based stakeholder engagement network could enable more online community meetings (n=19/37, 51%) and facilitate more opportunities to suggest, comment on, and vote for new CER ideas in COPD (n=18/37, 49%). CONCLUSIONS: Members of this unique virtual advisory board engaged in a structured Web-based communication process that identified the most important community-specific COPD care coordination research topics and questions. Findings from this study support the need for more CER that evaluates quality of care measures used to assess the delivery of treatments and interventions among medically underserved patients with COPD.

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 imitation

Not 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.

metaresearch head score (Codex)0.074
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.283
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0740.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.818
GPT teacher head0.728
Teacher spread0.090 · 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; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreProtocol

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

Citations5
Published2015
Admission routes1
Has abstractyes

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