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
Bibliographic record
Abstract
Abstract Background Tuberculosis (TB) has been regarded as “a relentless scourge” which considerably increases morbidity and mortality as well as bringing heavy burdens on the vulnerable populations. A novel approach to TB called “electronic monitors” seems promising as an intervention, improving adherence and health outcomes and overcoming the weaknesses of the traditional interventions. However, no review has systematically examined and synthesized the influencing factors of implementing electronic monitors. Implementation research offers the means to analyze the influencing factors of the implementation and its process, fitting well with the aim of this review. Therefore, framework-based implementation research will be adopted to systematically identify barriers and facilitators of the electronic monitors that aim to improve adherence and health outcomes in TB patients. Methods and Analysis: The systematic review will follow the PRISMA guidelines. Literature research will be conducted in five electronic databases (MEDLINE, CINAHL, EMBASE, Cochrane Library, and Web of Science) to identify the barriers and facilitators of implementing electronic monitors in TB patients. The Consolidated Framework for Implementation Research (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 an implementation science framework (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. Information provided by the review will be of essential significance for research and practice, supporting future academic research initiatives centered on TB patients and aiding the design of electronic monitors in lowering the morbidity and mortality associated with TB disease. Trial registration: PROSPERO: CRD42023395747.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.163 | 0.175 |
| Meta-epidemiology (narrow) | 0.009 | 0.008 |
| Meta-epidemiology (broad) | 0.025 | 0.030 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.050 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".