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Record W6931743806 · doi:10.5683/sp3/zzirt0

Search strategy for caregiver reported outcomes AND chronic illness

2022· dataset· en· W6931743806 on OpenAlexaff

Bibliographic record

VenueBorealis · 2022
Typedataset
Languageen
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsMcGill University
Fundersnot available
KeywordsCINAHLMEDLINEGrey literaturePsycINFOCochrane Library

Abstract

fetched live from OpenAlex

The notes field contains the full MEDLINE (Ovid) search strategy for caregiver reported outcomes AND chronic illness. The file in this dataset contains the full Ovid MEDLINE, Ovid Embase, Ovid PsycInfo, and Ebsco CINAHL search strategies for caregiver reported outcomes AND chronic illness. An iterative process was used for the data collection of this scoping review, with an initial screening done of the Ovid Medline search results (search date July 7 2021), and a second screening done of updated Ovid Medline search results as well as searches translated into Ovid Embase (search date August 27, 2021), Ovid PsycInfo, and Ebsco CINAHL (search date August 30, 2021). Also included in the file are details pertaining to the grey literature search including the search strategy used in Proquest Global Theses and Dissertations.

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)0.010
metaresearch head score (Gemma)0.057
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.990
Threshold uncertainty score0.941

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.057
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.004
Bibliometrics0.0280.038
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.3400.039

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 head0.060
GPT teacher head0.361
Teacher spread0.301 · 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 designNot applicable
DomainMethods
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

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