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Record W2158610893 · doi:10.1093/bjsw/bcv022

Comparative Performance of Adult Social Care Research, 1996–2011: A Bibliometric Assessment

2015· article· en· W2158610893 on OpenAlexaboutno aff
David Campbell, Grégoire Côté, Jonathan Grant, Martín Knapp, Anji Mehta, Molly Morgan Jones

Bibliographic record

VenueThe British Journal of Social Work · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)ScopusBibliometricsCitationCitation analysisPolitical scienceSocial scienceLibrary scienceSociologyComputer scienceMEDLINELaw

Abstract

fetched live from OpenAlex

Decision makers in adult social care are increasingly interested in using evidence from research to support or shape their decisions. The scope and nature of the current landscape of adult social care research (ASCR) need to be better understood. This paper provides a bibliometric assessment of ASCR outputs from 1996 to 2011. ASCR papers were retrieved using three strategies: from key journals; using keywords and noun phrases; and from additional papers preferentially citing or being cited by other ASCR papers. Overall, 195,829 ASCR papers were identified in the bibliographic database Scopus, of which 16 per cent involved at least one author from the UK. The UK output increased 2.45-fold between 1996 and 2011. Among selected countries, those with greater research intensity in ASCR generally had higher citation impact, such as the USA, UK, Canada and the Netherlands. The top five UK institutions in terms of volume of papers in the UK accounted for 26 per cent of total output. We conclude by noting the limitations to bibliometric analysis of ASCR and examine how such analysis can support the strategic development of the field.

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 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)0.024
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.731
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.008
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.505
GPT teacher head0.505
Teacher spread0.001 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
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

Citations4
Published2015
Admission routes1
Has abstractyes

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