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Record W2136240894 · doi:10.1177/1049732309348382

Patient Real-Time and 12-Month Retrospective Perceptions of Difficult Communications in the Cancer Diagnostic Period

2009· article· en· W2136240894 on OpenAlexaff
Sally Thorne, Elizabeth-Anne Armstrong, Susan R. Harris, T. Gregory Hislop, Charmaine Kim‐Sing, Valerie Oglov, John L. Oliffe, Kelli Stajduhar

Bibliographic record

VenueQualitative Health Research · 2009
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversity of VictoriaBC Cancer AgencyUniversity of British Columbia
Fundersnot available
KeywordsContext (archaeology)Period (music)Perspective (graphical)PerceptionCancerMedicineDiseaseRetrospective cohort studyPsychologyComputer sciencePathologyArtificial intelligence

Abstract

fetched live from OpenAlex

Communication is a notoriously complex challenge in the cancer care context. Our program of research involves exploration of patient-provider communications across the cancer trajectory from the patient perspective.Toward this end, we have been following a cohort of 60 cancer patients, representing a range of tumor sites, from immediately after diagnosis through to recovery, chronic, or advanced disease. Drawing on interpretive description analytic techniques, we documented patterns and themes related to various components of the cancer journey. In this article, we report on findings pertaining to poor communication during the initial diagnostic period, as described by patients at the time of diagnosis and 1 year later.These findings illuminate the dynamics of communication problems during that complex period, and depict the mechanisms by which patients sought to confront these challenges to optimize their cancer care experience. On the basis of these findings, considered in the context of the body of available evidence, suggestions are proposed as to appropriate directions for system-level solutions to the complex communication challenges within 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)0.007
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.496
GPT teacher head0.615
Teacher spread0.119 · 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 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

Citations50
Published2009
Admission routes1
Has abstractyes

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