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Record W2412446189 · doi:10.1177/082585971102700403

Evaluating Program Integration and the Rise in Collaboration: Case study of A Palliative Care Network

2011· article· en· W2412446189 on OpenAlexafffundabout
Daryl Bainbridge, Kevin Brazil, Paul Krueger, Jenny Ploeg, Alan Taniguchi, Julie Darnay

Bibliographic record

VenueJournal of Palliative Care · 2011
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsHealth Sciences CentreCanadian Hospice Palliative Care AssociationMcMaster University Medical CentreSt. Joseph’s Healthcare HamiltonPublic Health OntarioHamilton Health SciencesUniversity of TorontoMcMaster UniversityJuravinski Cancer Centre
FundersMcMaster UniversityHamilton Health Sciences
KeywordsGeneral partnershipPalliative careCitizen journalismAffect (linguistics)NursingParticipatory action researchProcess managementBusinessPsychologyMedicineSociologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

INTRODUCTION: There is increasing global interest in using regional palliative care networks (PCNs) to integrate care and create systems that are more cost-effective and responsive. We examined a PCN that used a community development approach to build capacity for palliative care in each distinct community in a region of southern Ontario, Canada, with the goal of achieving a competent integrated system. METHODS: Using a case study methodology, we examined a PCN at the structural level through a document review, a survey of 20 organizational administrators, and an interview with the network director. RESULTS: The PCN identified 14 distinct communities at different stages of development within the region. Despite the lack of some key features that would facilitate efficient palliative care delivery across these communities, administrators largely viewed the network partnership as beneficial and collaborative. CONCLUSION: The PCN has attempted to recognize specific needs in each local area. Change is gradual but participatory. There remain structural issues that may negatively affect the functioning of the PCN.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.248
GPT teacher head0.500
Teacher spread0.252 · 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.

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

Citations21
Published2011
Admission routes3
Has abstractyes

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