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Record W2258287894 · doi:10.1186/s13012-016-0373-y

Health systems guidance appraisal—a critical interpretive synthesis

2015· review· en· W2258287894 on OpenAlex

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

VenueImplementation Science · 2015
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMcMaster UniversityJuravinski Hospital
FundersPan American Health OrganizationCanadian Institutes of Health ResearchUniversidad de AntioquiaLanzhou UniversityMcMaster UniversityCancer Care OntarioNational Institute for Health and Care ExcellenceAmerican University of BeirutLondon School of Hygiene and Tropical MedicineWorld Health Organization
KeywordsCritical appraisalRelevance (law)Health informaticsData extractionGrey literatureProcess (computing)Computer scienceHealth administrationProcess managementMedicineManagement scienceData scienceKnowledge managementPublic healthMEDLINEPolitical scienceAlternative medicinePathologyEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Health systems guidance (HSG) are systematically developed statements that assist with decisions about options for addressing health systems challenges, including related changes in health systems arrangements. However, the development, appraisal, and reporting of HSG poses unique conceptual and methodological challenges related to the varied types of evidence that are relevant, the complexity of health systems, and the pre-eminence of contextual factors. To address this gap, we are conducting a program of research that aims to create a tool to support the appraisal of HSG and further enhance HSG development and reporting. The focus of this paper was to conduct a knowledge synthesis of the published and grey literatures to determine quality criteria (concepts) relevant for this process. METHODS: We applied a critical interpretive synthesis (CIS) approach to knowledge synthesis that enabled an iterative, flexible, and dynamic analysis of diverse bodies of literature in order to generate a candidate list of concepts that will constitute the foundational components of the HSG tool. Using our review questions as compasses, we were able to guide the search strategy to look for papers based on their potential relevance to HSG appraisal, development, and reporting. The search strategy included various electronic databases and sources, subject-specific journals, conference abstracts, research reports, book chapters, unpublished data, dissertations, and policy documents. Screening the papers and data extraction was completed independently and in duplicate, and a narrative approach to data synthesis was executed. RESULTS: We identified 43 papers that met eligibility criteria. No existing review was found on this topic, and no HSG appraisal tool was identified. Over one third of the authors implicitly or explicitly identified the need for a high-quality tool aimed to systematically evaluate HSG and contribute to its development/reporting. We identified 30 concepts that may be relevant to the appraisal of HSG and were able to cluster them into three meaningful domains: process principles, content, and context principles. CONCLUSIONS: Our study showed the role that the quality criteria play in the development, appraisal, and reporting of HSG and demonstrated the link and resonance within and between the various concepts in the three domains.

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)0.029
metaresearch head score (Gemma)0.056
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.850
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.056
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.004
Science and technology studies0.0030.001
Scholarly communication0.0000.002
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.782
GPT teacher head0.798
Teacher spread0.016 · 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