MétaCan
Menu
Back to cohort
Record W2887350754 · doi:10.1186/s13063-018-2796-z

Comparison of registered and published intervention fidelity assessment in cluster randomised trials of public health interventions in low- and middle-income countries: systematic review

2018· review· en· W2887350754 on OpenAlexafffund
Myriam Cielo Pérez, Nanor Minoyan, Valéry Ridde, Marie‐Pierre Sylvestre, Mira Johri

Bibliographic record

VenueTrials · 2018
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsInstitut National de Santé Publique du QuébecUniversité de MontréalCentre Hospitalier de l’Université de Montréal
FundersFonds de recherche du Québec
KeywordsMedicinePsychological interventionCRTSProtocol (science)CINAHLPublic healthFamily medicineClinical trialMEDLINEFidelityAlternative medicineNursingPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Cluster randomised trials (CRTs) are a key instrument to evaluate public health interventions. Fidelity assessment examines study processes to gauge whether an intervention was delivered as initially planned. Evaluation of implementation fidelity (IF) is required to establish whether the measured effects of a trial are due to the intervention itself and may be particularly important for CRTs of complex interventions conducted in low- and middle-income countries (LMICs). However, current CRT reporting guidelines offer no guidance on IF assessment. The objective of this review was to study current practices concerning the assessment of IF in CRTs of public health interventions in LMICs. METHODS: CRTs of public health interventions in LMICs that planned or reported IF assessment in either the trial protocol or the main trial report were included. The MEDLINE/PubMed, CINAHL and EMBASE databases were queried from January 2012 to May 2016. To ensure availability of a study protocol, CRTs reporting a registration number in the abstract were included. Relevant data were extracted from each study protocol and trial report by two researchers using a predefined screening sheet. Risk of bias for individual studies was assessed. RESULTS: We identified 90 CRTs of public health interventions in LMICs with a study protocol in a publicly available trial registry published from January 2012 to May 2016. Among these 90 studies, 25 (28%) did not plan or report assessing IF; the remaining 65 studies (72%) addressed at least one IF dimension. IF assessment was planned in 40% (36/90) of trial protocols and reported in 71.1% (64/90) of trial reports. The proportion of overall agreement between the trial protocol and trial report concerning occurrence of IF assessment was 66.7% (60/90). Most studies had low to moderate risk of bias. CONCLUSIONS: IF assessment is not currently a systematic practice in CRTs of public health interventions carried out in LMICs. In the absence of IF assessment, it may be difficult to determine if CRT results are due to the intervention design, to its implementation, or to unknown or external factors that may influence results. CRT reporting guidelines should promote IF assessment. TRIAL REGISTRATION: Protocol published and available at: https://doi.org/10.1186/s13643-016-0351-0.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Reporting · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewlow
gptMetaresearch
Domain: Reporting · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.405
metaresearch head score (Gemma)0.190
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.215
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.4050.190
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0230.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.883
GPT teacher head0.745
Teacher spread0.138 · 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

Labeled directly by 2 models reading the full record.

Study designSystematic review
DomainReporting
GenreReview

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

Citations24
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueTrialsSame topicHealth Policy Implementation ScienceCategoryMetaresearchFrench-language works237,207