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Record W2057731801 · doi:10.3747/co.21.1930

Patient Perceptions of a Comprehensive Cancer Navigation Service

2014· article· en· W2057731801 on OpenAlexaffvenue
W M Hryniuk, Rosemary Simpson, A McGowan, Paul Carter

Bibliographic record

VenueCurrent Oncology · 2014
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsCancer Care Ontario
Fundersnot available
KeywordsMedicinePerceptionFamily medicineNavigation systemService (business)NursingPsychologyMarketing

Abstract

fetched live from OpenAlex

OBJECTIVE: Our aim was to determine the extent to which comprehensive navigation augments the provincial health system for meeting the needs of newly-diagnosed cancer patients (clients). We also assessed reactions of attending physicians to comprehensive navigation. METHODS: Clients who completed navigation as an employee benefit or through membership in an insurance organization were polled to determine whether they needed help beyond that provided by the provincial health system and the extent to which that help was provided by navigation. Exit interviews were analyzed for perceptions of the clients about reactions by their attending physicians to navigation. RESULTS: Of eligible clients, 72% responded. They reported needing help beyond that which the provincial system could provide in 64%-98% of specified areas. Navigation provided help in more than 90% of those cases. Almost all respondents (98%) appreciated having a designated oncology nurse navigator. Family doctors were perceived to be positive or neutral about navigation in 100% of exit interviews. Oncologists were positive or neutral in 92% (p < 0.001 for difference from family doctors). CONCLUSIONS: In many areas, cancer patients need additional help beyond that which the provincial health system can provide. Comprehensive cancer navigation provides that help to a considerable extent. Clients perceived the reactions of attending physicians to comprehensive navigation to be generally supportive or neutral.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.253

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 head0.184
GPT teacher head0.462
Teacher spread0.278 · 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 designOther design
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
Published2014
Admission routes2
Has abstractyes

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