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Record W2805807840 · doi:10.1080/17571472.2018.1477455

Evaluating case studies of community-oriented integrated care

2018· article· en· W2805807840 on OpenAlexaff
Paul Thomas, Amrit Sachar, Andrew Papanikitas, Alison While, Chris Brophy, Chris Manning, C. Wright Mills, Baljeet Ruprah-Shah, Catherine Millington‐Sanders, David Morris, Deirdre Kelley Patterson, Diana Hill, Emma McKenzie-Edwards, Fiona Wright, Francesco Carelli, Freddy Shaw, Isabelle Vedel, John Spicer, Elizabeth Wewiora, Malik Muhammad Usman Gul, Michelle Kirkbride BA, Mike Sadlowski, Mylaine Breton, Ricky Banarsee, Sunjai Gupta, Tony Burch, Tulloch Kempe, Victoria Tzortziou Brown, John Sanfey

Bibliographic record

VenueLondon Journal of Primary Care · 2018
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversité de SherbrookeMcGill University
FundersNational Institute for Health and Care Research
KeywordsConversationWarrantIntegrated careHealth careLocal communityCommunity healthPublic relationsKnowledge managementComputer scienceMedicineNursingSociologyBusinessPublic healthPolitical science

Abstract

fetched live from OpenAlex

This paper summarises a ten-year conversation within London Journal of Primary Care about the nature of community-oriented integrated care (COIC) and how to develop and evaluate it. COIC means integration of efforts for combined disease-treatment and health-enhancement at local, community level. COIC is similar to the World Health Organisation concept of a Community-Based Coordinating Hub - both require a local geographic area where different organisations align their activities for whole system integration and develop local communities for health. COIC is a necessary part of an integrated system for health and care because it enables multiple insights into 'wicked problems', and multiple services to integrate their activities for people with complex conditions, at the same time helping everyone to collaborate for the health of the local population. The conversation concludes seven aspects of COIC that warrant further attention.

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)0.057
metaresearch head score (Gemma)0.098
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.303

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0570.098
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0100.008
Scholarly communication0.0090.008
Open science0.0060.014
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.113
GPT teacher head0.516
Teacher spread0.403 · 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 designQualitative
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

Citations2
Published2018
Admission routes1
Has abstractyes

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