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Record W2735737337 · doi:10.5334/ijic.2543

Integrated Care Planning for Cancer Patients: A Scoping Review

2017· review· en· W2735737337 on OpenAlexaff
Anum Irfan Khan, Erin Arthurs, Sharon Gradin, Marnie MacKinnon, Jonathan Sussman, Vishal Kukreti

Bibliographic record

VenueInternational Journal of Integrated Care · 2017
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkMcMaster UniversityCancer Care OntarioInstitute for Work & Health
Fundersnot available
KeywordsIntegrated careMedicineHealth careNursingProcess managementBusinessPolitical science

Abstract

fetched live from OpenAlex

INTRODUCTION: There has been a growing emphasis on the use of integrated care plans to deliver cancer care. However little is known about how integrated care plans for cancer patients are developed including featured core activities, facilitators for uptake and indicators for assessing impact. METHODS: Given limited consensus around what constitutes an integrated care plan for cancer patients, a scoping review was conducted to explore the components of integrated care plans and contextual factors that influence design and uptake. RESULTS: Five types of integrated care plans based on the stage of cancer care: surgical, systemic, survivorship, palliative and comprehensive (involving a transition between stages) are described in current literature. Breast, esophageal and colorectal cancers were common disease sites. Multi-disciplinary teams, patient needs assessment and transitional planning emerged as key features. Provider buy-in and training alongside informational technology support served as important facilitators for plan uptake. Provider-level measurement was considerably less robust compared to patient and system-level indicators. CONCLUSIONS: Similarities in design features, components and facilitators across the various types of integrated care plans indicates opportunities to leverage shared features and enable a management lens that spans the trajectory of a patient's journey rather than a phase-specific silo approach to care.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.895
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.092
GPT teacher head0.449
Teacher spread0.357 · 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 designOther design
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

Citations30
Published2017
Admission routes1
Has abstractyes

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