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Record W2786916826 · doi:10.2196/resprot.8660

Motivational Interviewing and Medication Review in Coronary Heart Disease (MIMeRiC): Intervention Development and Protocol for the Process Evaluation

2018· article· en· W2786916826 on OpenAlexvenueno aff
Malin Johansson Östbring, Tommy Eriksson, Göran Petersson, Lina Hellström

Bibliographic record

VenueJMIR Research Protocols · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsMotivational interviewingPsychological interventionIntervention (counseling)Context (archaeology)Randomized controlled trialMedicineQualitative researchProtocol (science)PsychologyAlternative medicineNursingPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Trials of complex interventions are often criticized for being difficult to interpret because the effects of apparently similar interventions vary across studies dependent on context, targeted groups, and the delivery of the intervention. The Motivational Interviewing and Medication Review in Coronary heart disease (MIMeRiC) trial is a randomized controlled trial (RCT) of an intervention aimed at improving pharmacological secondary prevention. Guidelines for the development and evaluation of complex interventions have recently highlighted the need for better reporting of the development of interventions, including descriptions of how the intervention is assumed to work, how this theory informed the process evaluation, and how the process evaluation relates to the outcome evaluation. OBJECTIVE: This paper aims to describe how the intervention was designed and developed. The aim of the process evaluation is to better understand how and why the intervention in the MIMeRiC trial was effective or not effective. METHODS: The research questions for evaluating the process are based on the conceptual model of change processes assumed in the intervention and will be analyzed by qualitative and quantitative methods. Quantitative data are used to evaluate the medication review in terms of drug-related problems, to describe how patients' beliefs about medicines are affected by the intervention, and to evaluate the quality of motivational interviewing. Qualitative data will be used to analyze whether patients experienced the intervention as intended, how cardiologists experienced the collaboration and intervention, and how the intervention affected patients' overall experience of care after coronary heart disease. RESULTS: The development and piloting of the intervention are described in relation to the theoretical framework. Data for the process evaluation will be collected until March 2018. Some process evaluation questions will be analyzed before, and others will be analyzed after the outcomes of the MIMeRiC RCT are known. CONCLUSIONS: This paper describes the framework for the design of the intervention tested in the MIMeRiC trial, development of the intervention from the pilot stage to the complete trial intervention, and the framework and methods for the process evaluation. Providing the protocol of the process evaluation allows prespecification of the processes that will be evaluated, because we hypothesize that they will determine the outcomes of the MIMeRiC trial. This protocol also constitutes a contribution to the new field of process evaluations as made explicit in health services research and clinical trials of complex interventions.

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)0.127
metaresearch head score (Gemma)0.132
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.127
Threshold uncertainty score0.674

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1270.132
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0050.006
Science and technology studies0.0040.004
Scholarly communication0.0040.004
Open science0.0040.004
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0420.012

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 head0.441
GPT teacher head0.669
Teacher spread0.228 · 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 designNot applicable
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

Citations6
Published2018
Admission routes1
Has abstractyes

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