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Record W4221137049 · doi:10.1007/s43477-022-00038-3

A Research Protocol for Implementation and Evaluation of a Patient-Focused eHealth Intervention for Chronic Kidney Disease

2022· article· en· W4221137049 on OpenAlexafffund
Maoliosa Donald, Heather Beanlands, Sharon E. Straus, Lori Harwood, Gwen Herrington, Blair Waldvogel, María Delgado, Dwight Sparkes, Paul Watson, Meghan J. Elliott, Kerry McBrien, Aminu K. Bello, Brenda R. Hemmelgarn

Bibliographic record

VenueGlobal Implementation Research and Applications · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of AlbertaLondon Health Sciences CentreToronto Metropolitan UniversityUniversity of TorontoCanadian Association of Nurses in OncologyUniversity of Calgary
FundersEconomic and Social Research CouncilEngineering and Physical Sciences Research CouncilCanadian Institutes of Health ResearchVillum Fonden
KeywordseHealthContext (archaeology)Implementation researchProcess managementMedicineQuality managementHealth careQualitative researchSustainabilityQuality (philosophy)Intervention (counseling)Knowledge managementNursingComputer sciencePsychological interventionOperations managementEngineeringManagement system

Abstract

fetched live from OpenAlex

Abstract Self-management in chronic kidney disease (CKD) can slow disease progression; however, there are few tools available to support patients with early CKD. My Kidneys My Health is a patient-focused electronic health (eHealth) self-management tool developed by patients and caregivers. This study will investigate the implementation of My Kidneys My Health across primary care and general nephrology clinics. The study aims to: (1) identify and address barriers and facilitators that may impact implementation and sustainability of the website into routine clinical care; (2) evaluate implementation quality to inform spread and scale-up. We will conduct a multi-stage approach using qualitative methods, guided by the Quality Implementation Framework and using a qualitative content analysis approach. First, we will identify perceived barriers and facilitators to implementation and considerations for sustainability through interviews with clinicians, based on the Readiness Thinking Tool and the Long Term Success Tool. Analysis will be guided by the Consolidated Framework for Implementation Research and the Theoretical Domains Framework. Appropriate implementation strategies will be identified using the Expert Recommendations for Implementing Change compilation, and implementation plans will be developed based on Proctor’s recommendations and the Action, Actor, Context, Target, Time framework. Finally, we will explore implementation quality guided by the RE-AIM framework. There is limited literature describing systematic approaches to implementing and sustaining patient-focused self-management tools into clinical care, in addition to employing tailored implementation strategies to promote adoption and sustainability. We aim to generate insights on how My Kidneys My Health can be integrated into clinical care and how to sustain use of patient-centric eHealth tools in clinical settings on a larger scale.

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)0.152
metaresearch head score (Gemma)0.115
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 score0.152
Threshold uncertainty score0.802

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1520.115
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0050.004
Science and technology studies0.0090.004
Scholarly communication0.0050.005
Open science0.0050.005
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.1050.023

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 head0.705
GPT teacher head0.783
Teacher spread0.078 · 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

Citations9
Published2022
Admission routes2
Has abstractyes

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