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Record W2015938947 · doi:10.3109/13561820902921829

Multidisciplinary cancer conferences: Exploring the attitudes of cancer care providers and administrators

2009· article· en· W2015938947 on OpenAlexaffabout
Nicole J. Look Hong, Frances C. Wright, Anna R. Gagliardi, Patrick Brown, Mark Dobrow

Bibliographic record

VenueJournal of Interprofessional Care · 2009
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsHealth Sciences CentreCancer Care OntarioSunnybrook Health Science Centre
Fundersnot available
KeywordsMultidisciplinary approachMedicineCancerFamily medicineNursing

Abstract

fetched live from OpenAlex

The multidisciplinary cancer conference (MCC) provides an outlet for contributors in cancer care collectively to evaluate diagnosis and treatment options and to provide optimal patient care. The prevalence and perceived benefits of MCCs in Canada have not previously been described. Between February and March 2007, the Cancer Services Integration Survey, including four key statements concerning MCCs, was administered to cancer care providers and administrators in Ontario, Canada. A total of 1,769 responses were received with a response rate of 33%. Overall, 74% of respondents were aware of MCCs within their region, but only 58% were either regular MCC participants, or acknowledged participation of cancer providers in their institutions. Using multilevel modeling, physicians (OR 2.69, p-value < 0.01, 95% CI 1.62-4.57) and surgeons (OR 3.00, p-value < 0.01, 95% CI 1.52-6.20) both perceived greater benefit of MCCs for coordinating and improving patient plans than administrators. Although MCCs appear to positively influence patient care and interprofessional interactions, variability exists among cancer providers and administrators concerning their acceptance and perceived benefits. Further research should concentrate on further probing these trends, and exploring explanations and solutions for the inconsistent acceptance of MCCs into routine cancer 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.105
GPT teacher head0.431
Teacher spread0.326 · 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 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

Citations22
Published2009
Admission routes2
Has abstractyes

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