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Record W2073451449 · doi:10.1002/pon.945

‘Being known’: patients' perspectives of the dynamics of human connection in cancer care

2005· article· en· W2073451449 on OpenAlexaff
Sally Thorne, Margot Kuo, Elizabeth-Anne Armstrong, Gladys McPherson, Susan R. Harris, T. Gregory Hislop

Bibliographic record

VenuePsycho-Oncology · 2005
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversity of British Columbia
FundersNational Cancer Institute
KeywordsContext (archaeology)Perspective (graphical)Connection (principal bundle)CancerAnonymityPsychologyBreast cancerMedicineComputer scienceArtificial intelligenceHistoryEngineering

Abstract

fetched live from OpenAlex

In the context of a large study of effective and ineffective cancer care communications from the perspective of patients with cancer, the authors documented the pervasiveness of the desire for human connection. Analyzing accounts from 200 patients with diverse cancer experiences, they concluded that, while anonymity is generally antithetical to a comfortable cancer care encounter, there are wide variations in what it means to 'be known' in a meaningful way. In this discussion, a description of the dynamics of being known and not being known within the cancer care encounter is presented, and a range of variations considered. By illuminating the manner in which communication influences human connection within the cancer care context, the findings of this study challenge some current research directions and propose alternative conceptualizations that might better orient future inquiry to enhance practice.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0140.025
Scholarly communication0.0130.013
Open science0.0020.010
Research integrity0.0050.011
Insufficient payload (model declined to judge)0.0030.000

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 head0.107
GPT teacher head0.480
Teacher spread0.373 · 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 source (direct Gemma or distilled Codex), 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

Citations156
Published2005
Admission routes1
Has abstractyes

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