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Record W2031744068 · doi:10.1177/0163278713511325

Evidence-Based Health Care Management

2013· review· en· W2031744068 on OpenAlexaff
Mirou Jaana, Smruti Vartak, Marcia M. Ward

Bibliographic record

VenueEvaluation & the Health Professions · 2013
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of Ottawa
FundersAgency for Healthcare Research and Quality
KeywordsHealth carePsychological interventionWorkforceMEDLINEInclusion (mineral)MedicineRelevance (law)Quality managementEvidence-based practiceSystematic reviewNursingBusinessAlternative medicinePsychologyPolitical scienceMarketing

Abstract

fetched live from OpenAlex

In light of increasing interest in evidence-based management, we conducted a scoping review of systematic reviews (SRs) and meta-analyses (MAs) to determine the availability and accessibility of evidence for health care managers; 14 MAs and 61 SRs met the inclusion criteria. Most reviews appeared in medical journals (53%), originated in the United States (29%) or United Kingdom (22%), were hospital-based (55%), and targeted clinical providers (55%). Topics included health services organization (34%), quality/patient safety (17%), information technology (15%), organization/workplace management (13%), and health care workforce (12%). Most reviews addressed clinical topics of relevance to managers; management-related interventions were rare. The management issues were mostly classified as operational (65%). Surprisingly, 96.5% of search results were not on target. A better classification within PubMed is needed to increase the accessibility of meaningful resources and facilitate evidence retrieval. Health care journals should take initiatives encouraging the publication of reviews in relevant management areas.

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)0.110
metaresearch head score (Gemma)0.281
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.110
Threshold uncertainty score0.582

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1100.281
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0250.016
Science and technology studies0.0020.002
Scholarly communication0.0120.009
Open science0.0050.007
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0090.002

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 head0.832
GPT teacher head0.607
Teacher spread0.225 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
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

Citations33
Published2013
Admission routes1
Has abstractyes

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