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Record W2974443830 · doi:10.1097/xeb.0000000000000202

Evaluation of the JBI scoping reviews methodology by current users

2019· article· en· W2974443830 on OpenAlexaff
Hanan Khalil, Marsha Bennett, Christina Godfrey, Patricia McInerney, Zac Munn, Micah D.J. Peters

Bibliographic record

VenueInternational Journal of Evidence-Based Healthcare · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsQueen's University
Fundersnot available
KeywordsSystematic reviewDescriptive statisticsMedical educationWork (physics)PsychologyMEDLINEMedicineEngineeringPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: In 2014, JBI Database of Systematic Reviews and Implementation Reports published a comprehensive methodology for the conduct and reporting of scoping reviews based on previous frameworks and guidance. Further work on scoping review methodology and particularly reporting is needed. To assist with refinements to the methodology, this survey was undertaken to evaluate users' experiences of following the process methodology. An electronic survey was generated to explore authors' experiences with the methodology and to seek feedback on the stages of scoping review development. METHOD: An online survey administered using Qualtrics - a secure survey platform - was distributed through invitations to a total of 51 registered users in the Joanna Briggs Database of Systematic reviews and Implementation reports. We analysed the questionnaire data using descriptive statistics. The qualitative data were grouped together, and free text comments were inductively themed and coded by the authors. RESULTS: Thirty-one participants completed the survey (response rate of 61%). The majority of the participants identified themselves as researchers (55%) followed by educators (25%). Most participants were university employees (77%) and only 10% were based in hospitals. Forty-two percent of the participants reported that the scoping review they had been involved with had taken between 6 and 12 months, and 32% of participants spent over a year completing their reviews. Eighty-seven percent of participants stated that their scoping reviews led to further work such as developing a systematic review, a basis for a grant application, formation of a part of students' doctoral studies, and informing further work in a research project. Some of the limitations listed by the participants were the lack of examples in each section of the methodology, especially in the inclusion criteria, and presentation of the results sections. CONCLUSION: The overall evaluation by the participants of the JBI scoping review methodology highlighted the need for additional detailed guidance for inclusion criteria and presentation of the results. Provision of clear examples for each step was also requested for future improvement.

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)0.728
metaresearch head score (Gemma)0.847
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.272
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7280.847
Meta-epidemiology (narrow)0.0020.005
Meta-epidemiology (broad)0.0050.007
Bibliometrics0.0260.028
Science and technology studies0.0050.006
Scholarly communication0.0220.012
Open science0.0060.018
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0150.009

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 head0.964
GPT teacher head0.697
Teacher spread0.267 · 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.

Study designObservational
DomainMethods
GenreEmpirical

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

Citations86
Published2019
Admission routes1
Has abstractyes

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