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Record W2941490762 · doi:10.2196/13317

Designing and Testing a Treatment Adherence Model Based on the Roy Adaptation Model in Patients With Heart Failure: Protocol for a Mixed Methods Study

2019· article· en· W2941490762 on OpenAlexvenueno aff
Shabnam Shariatpanahi, Mansoureh Ashghali Farahani, Maryam Rassouli, Amir Kavousi

Bibliographic record

VenueJMIR Research Protocols · 2019
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsnot available
FundersIran University of Medical Sciences
KeywordsContext (archaeology)Automatic summarizationQualitative researchProtocol (science)PsychologyContent analysisData collectionStructural equation modelingConceptual modelResearch designExploratory researchAdaptation (eye)Clinical psychologyApplied psychologyMedicineComputer scienceAlternative medicineArtificial intelligenceStatisticsMachine learning

Abstract

fetched live from OpenAlex

BACKGROUND: Adherence to treatment is an important factor to decrease repeated and costly hospitalization owing to heart failure (HF). The explanation and prediction of medication adherence and other lifestyle recommendations in chronic diseases, including HF, are complex. Theories lead to a better understanding of complex situations as well as the process of changing behavior and explain the reasons for the existence of a problem. OBJECTIVE: The aim of this study is to report a protocol for a mixed methods study setting out to investigate the empirical validity of the Roy Adaptation Model as a conceptual framework for explaining and predicting adherence to treatment in patients with HF in Iran. METHODS: This mixed methods study consists of an exploratory sequential design to be conducted in 2 phases. The first phase involves identifying the factors associated with treatment adherence in patients with HF through content analysis of the literature and elucidating the perception of participants in the context of Iranian health care where the model of adherence to treatment is designed based on the Roy Adaptation Model. The second phase addresses the interrelationships among variables in the model through a descriptive study using structural equation modeling. Finally, following the summarization and separate interpretation of the qualitative findings and quantitative results, a decision is made about the extent to and ways in which the results of the quantitative stage can be generalized or tested for the qualitative findings. RESULTS: Content analysis of the literature in part 1 of the first phase was completed in 2017. Collection and analysis of qualitative data in part 2 of the first phase will be completed soon. The results are expected to be submitted for publication in 2019. Then, the second phase-the quantitative study-will be conducted. CONCLUSIONS: The results of this study will provide valuable information about the empirical validity of the Roy Adaptation Model as a conceptual framework for explaining and predicting adherence to treatment in patients with HF, which, to date, have received little attention. The results can be used as a guide for nursing practice and care provision to patients with HF and also to design and implement effective interventions to improve treatment adherence in these patients. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/13317.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.888
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.511
GPT teacher head0.581
Teacher spread0.070 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations4
Published2019
Admission routes1
Has abstractyes

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