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Record W2167493123 · doi:10.1177/1049732311399778

Patient Decision Making Among Older Individuals With Cancer

2011· article· en· W2167493123 on OpenAlexafffund
Fay J. Strohschein, Howard Bergman, Franco A. Carnevale, Carmen G. Loiselle

Bibliographic record

VenueQualitative Health Research · 2011
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsContext (archaeology)Psychological interventionPsychologyQuality of life (healthcare)Maturity (psychological)Quality (philosophy)Decision-makingApplied psychologyDevelopmental psychologyPsychotherapistPsychiatryOperations management

Abstract

fetched live from OpenAlex

Patient decision making is an area of increasing inquiry. For older individuals experiencing cancer, variations in health and functional status, physiologic aspects of aging, and tension between quality and quantity of life present unique challenges to treatment-related decision making. We used the pragmatic utility method to analyze the concept of patient decision making in the context of older individuals with cancer. We first evaluated its maturity in existing literature and then posed analytical questions to clarify aspects found to be only partially mature. In this context, we found patient decision making to be an ongoing process, changing with time, reflecting individual and relational components, as well as analytical and emotional ones. Assumptions frequently associated with patient decision making were not consistent with the empirical literature. Careful attention to the multifaceted components of patient decision making among older individuals with cancer provides guidance for research, supportive interventions, and targeted follow-up 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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.755
GPT teacher head0.661
Teacher spread0.094 · 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

Citations16
Published2011
Admission routes2
Has abstractyes

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