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Record W2002893163 · doi:10.3747/co.v17i4.527

Exploring Cancer Treatment Decision-Making by Patients: A Descriptive Study

2010· article· en· W2002893163 on OpenAlexaffvenueabout
Dawn Stacey, Lise Paquet, Rajiv Samant

Bibliographic record

VenueCurrent Oncology · 2010
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsCarleton UniversityOttawa HospitalOttawa Regional Cancer FoundationUniversity of Ottawa
Fundersnot available
KeywordsMedicineScale (ratio)Patient participationFamily medicineDescriptive statisticsCancerHealth careInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Using an interview-guided survey, our descriptive study aimed to document the extent to which cancer patients perceive they are involved in making treatment decisions and the factors that influence patient involvement. PATIENTS AND METHODS: Our study enrolled patients from a Canadian ambulatory oncology program who were undergoing chemotherapy or radiation therapy, or both, for cancer. The adapted Control Preferences Scale was used to survey perceived and preferred roles in decision-making. The study survey also included items from the Decisional Conflict Scale and the Preparation for Decision-Making Scale. RESULTS: Of 192 participants, 98 (51%) perceived that they were offered treatment choices. Of those 98, 47 (48%) thought that the options were presented equally. Compared with the patients not offered choices, those who were given choices were less passive (4% vs. 29%, p < 0.001) and more satisfied (100% vs. 95%, p < 0.03) in decision-making. Participants whose preferred and perceived roles were different would have preferred more involvement in decision-making. To attain the preferred involvement, patients wanted to receive more information on treatment options, to be given a choice, to have more discussion with the health care team, and to have providers better listen to their needs. CONCLUSIONS: Only half of surveyed patients thought that they were offered choices for their cancer treatment. When offered choices, patients were more active in decision-making. Further initiatives are required to determine approaches for supporting patients with cancer so that they can be more involved in decision-making.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.661
GPT teacher head0.568
Teacher spread0.093 · 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

Citations56
Published2010
Admission routes3
Has abstractyes

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