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Record W3000579834 · doi:10.1186/s13643-019-1258-3

Essential items for reporting of scaling studies of health interventions (SUCCEED): protocol for a systematic review and Delphi process

2020· review· en· W3000579834 on OpenAlex
Amédé Gogovor, Hervé Tchala Vignon Zomahoun, Ali Ben Charif, Robert K. D. McLean, David Moher, Andrew Milat, Luke Wolfenden, Karina Prévost, Emmanuelle Aubin, Paula A. Rochon, Giraud Ekanmian, Jasmine Sawadogo, Nathalie Rhéault, France Légaré

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSystematic Reviews · 2020
Typereview
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsFirst Nations of Quebec and Labrador Health and Social Services CommissionWomen's College HospitalUniversity of TorontoCanadian Patient Safety InstituteOttawa HospitalInternational Development Research CentreUniversité Laval
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsMedicinePsycINFODelphi methodCINAHLProtocol (science)ChecklistSystematic reviewMEDLINEMedical educationCochrane LibraryDelphiPsychological interventionAlternative medicineNursingPsychologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: The lack of a reporting guideline for scaling of evidence-based practices (EBPs) studies has prompted the registration of the Standards for reporting studies assessing the impact of scaling strategies of EBPs (SUCCEED) with EQUATOR Network. The development of SUCCEED will be guided by the following main steps recommended for developing health research reporting guidelines. METHODS: Executive Committee. We established a committee composed of members of the core research team and of an advisory group. Systematic review. The protocol was registered with the Open Science Framework on 29 November 2019 (https://osf.io/vcwfx/). We will include reporting guidelines or other reports that may include items relevant to studies assessing the impact of scaling strategies. We will search the following electronic databases: EMBASE, PsycINFO, Cochrane Library, CINAHL, Web of Science, from inception. In addition, we will systematically search websites of EQUATOR and other relevant organizations. Experts in the field of reporting guidelines will also be contacted. Study selection and data extraction will be conducted independently by two reviewers. A narrative analysis will be conducted to compile a list of items for the Delphi exercise. CONSENSUS PROCESS: We will invite panelists with expertise in: development of relevant reporting guidelines, methodologists, content experts, patient/member of the public, implementers, journal editors, and funders. We anticipated that three rounds of web-based Delphi consensus will be needed for an acceptable degree of agreement. We will use a 9-point scale (1 = extremely irrelevant to 9 = extremely relevant). Participants' response will be categorized as irrelevant (1-3), equivocal (4-6) and relevant (7-9). For each item, the consensus is reached if at least 80% of the participants' votes fall within the same category. The list of items from the final round will be discussed at face-to-face consensus meeting. Guideline validation. Participants will be authors of scaling studies. We will collect quantitative (questionnaire) and qualitative (semi-structured interview) data. Descriptive analyses will be conducted on quantitative data and constant comparative techniques on qualitative data. DISCUSSION: Essential items for reporting scaling studies will contribute to better reporting of scaling studies and facilitate the transparency and scaling of evidence-based health interventions.

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.

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)0.086
metaresearch head score (Gemma)0.153
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 score0.067
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0860.153
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0230.003
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.647
GPT teacher head0.681
Teacher spread0.034 · 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