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Record W4388127769 · doi:10.1186/s12961-023-01054-x

Barriers and facilitators of implementing electronic monitors to improve adherence and health outcomes in tuberculosis patients: protocol for a systematic review based on the Consolidated Framework for Implementation Research

2023· review· en· W4388127769 on OpenAlexaff
Wenhui Li, Min Su, Weile Zhang, Xiaojing Fan, Renzhong Li, Yulong Gao, Xiaolin Wei

Bibliographic record

VenueHealth Research Policy and Systems · 2023
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersNatural Science Foundation of Inner MongoliaNational Natural Science Foundation of ChinaNatural Science Foundation of Shanghai
KeywordsImplementation researchMedicinePsychological interventionHealth services researchTuberculosisHealth informaticsHealth administrationProtocol (science)Intervention (counseling)Public healthProcess managementMedical emergencyNursingAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Tuberculosis (TB) has been regarded as 'a relentless scourge', increasing morbidity and mortality and burdening vulnerable populations. Poor adherence to TB treatment and ineffective traditional interventions hinders TB control. A novel TB approach called 'electronic monitors', equipping medication boxes with daily audio or visual reminders for electronically monitoring medication intake, seems promising in improving adherence and health outcomes and overcoming the weaknesses of traditional interventions. However, no review has systematically examined and synthesized the influencing factors of implementing electronic monitors. Implementation research offers the means to analyse the influencing factors of the implementation and its process, fitting well with the aim of this review. Therefore, the widely recognized Consolidated Framework for Implementation Research (CFIR), which offers a common taxonomy for evaluating intervention implementation, will be adopted to systematically identify barriers and facilitators of the electronic monitors for improving adherence and health outcomes in patients with TB. METHODS AND ANALYSIS: The systematic review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Literature research will be conducted in five electronic databases (Ovid MEDLINE, CINAHL, EMBASE, Cochrane Library and Web of Science) to identify the barriers and facilitators of implementing electronic monitors in patients with TB. The CFIR will be used as a guide for categorizing and synthesizing the barriers and facilitators. Study screening, data extraction, quality appraisal and data analysis will be conducted by two independent reviewers. The use of additional reviewers will solve any disagreements between the two reviewers. DISCUSSION: Given the increased prominence of TB epidemiology and the adherence problem of electronic monitors, there is a solid rationale for synthesizing the existing studies via the CFIR. The findings and conclusion of this review will lay bare the achievements and effectiveness of implementing electronic monitors, as well as the attendant gaps and limitations. Further strategies for facilitating the implementation of electronic monitors will also be explored. This review will be of essential significance for research and practice, supporting future academic research initiatives centred on patients with TB and aiding electronic monitor design in lowering the morbidity and mortality associated with TB disease. TRIAL REGISTRATION NUMBER: PROSPERO: CRD42023395747.

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)0.133
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.127
Threshold uncertainty score0.673

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1270.133
Meta-epidemiology (narrow)0.0090.008
Meta-epidemiology (broad)0.0210.024
Bibliometrics0.0160.017
Science and technology studies0.0060.007
Scholarly communication0.0090.011
Open science0.0070.008
Research integrity0.0100.011
Insufficient payload (model declined to judge)0.0610.009

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 head0.476
GPT teacher head0.654
Teacher spread0.178 · 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 designSystematic review
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

Citations10
Published2023
Admission routes1
Has abstractyes

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