MétaCan
Menu
Back to cohort
Record W2335862351 · doi:10.2196/resprot.5376

Transforming the Patient Role to Achieve Better Outcomes Through a Patient Empowerment Program: A Randomized Wait-List Control Trial Protocol

2016· article· en· W2335862351 on OpenAlexvenueno aff
Lisa Altshuler, Joseph Plaksin, Sondra Zabar, Andrew Wallach, Chester Sawicki, Sarita Kundrod, Adina Kalet

Bibliographic record

VenueJMIR Research Protocols · 2016
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
Fundersnot available
KeywordsEmpowermentIntervention (counseling)Randomized controlled trialMedicineHealth carePatient participationFamily medicineNursingHealth literacyPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: In the patient-centered medical home model of health care, both health care providers (HCPs) and patients must understand their respective roles and responsibilities, view the other as a partner, and use communication skills that promote shared decision making. This is particularly necessary in chronic conditions where outcomes depend on behavior change and in underserved populations where the burden of chronic disease is high. OBJECTIVE: The objectives of this study are to determine if a Patient Empowerment Program (PEP) (1) is acceptable to patients and feasible across multiple clinical sites; (2) will increase patient preference for control in medical decision making, improve patient perceptions of patient-HCP communication, and increase patient activation; (3) is associated with an increase in diabetes self-management behaviors; and (4) has an effect on hemoglobin A1c (HbA1c) level. METHODS: This study recruited English-speaking adult patients with type 2 diabetes mellitus from three urban clinical sites in New York City and randomized them to an immediate intervention group that completed the PEP intervention or a deferred intervention group that served as a wait-list control and completed the PEP intervention after 3-4 months. The PEP intervention consists of two facilitated small group sessions. Session 1 focuses on defining HCP and patient roles in the medical encounter by introducing ideal communication behaviors in each role and by providing both positive and negative examples of patient-HCP encounters. Session 2 focuses on practicing communication skills by role-playing with actors who serve as standardized health care providers. After the role play, participants set goals for their own health care and for future interactions with their HCPs. Outcome measures include the Patient Activation Measure; Ask, Understand, Remember Assessment; Krantz Health Opinion Survey; SF-12v2 Health Survey; Diabetes Self-Management Questionnaire; and HbA1c. These measures will be assessed at the time of enrollment, after the waiting period (deferred intervention only), and then postintervention at 1 week, 3 months, and 6 months. RESULTS: Study recruitment occurred from November 2014 to June 2015, with a total of 80 patients enrolled. To date, 45 participants have attended at least one session of the PEP intervention. Further intervention sessions and post-intervention follow-up are ongoing, with data collection set to be completed in April 2016 and results of data analysis available by June 2016. CONCLUSIONS: From preliminary participant self-report data, our PEP intervention is acceptable to low-income, low-health literate patients and feasible to hold across multiple clinical sites. Participants have reported learning specific ways to change their behaviors at their next HCP visit (eg, stating their opinions, asking more questions). With the forthcoming quantitative data on participant attitudinal and behavior change, the PEP intervention may ultimately empower participants within the medical encounter and improve health outcomes.

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)0.012
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.045
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.012
Meta-epidemiology (narrow)0.0060.002
Meta-epidemiology (broad)0.0080.003
Bibliometrics0.0030.002
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0450.006

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 head0.095
GPT teacher head0.500
Teacher spread0.405 · 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 designRandomized trial
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

Citations16
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Research ProtocolsSame topicDiabetes Management and EducationFrench-language works237,207