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Record W4394997254 · doi:10.1016/j.pec.2024.108285

The role of time in involving patients with cancer in treatment decision making: A scoping review

2024· review· en· W4394997254 on OpenAlexaff
Thomas H. Wieringa, Montserrat León‐García, Nataly R. Espinoza Suárez, María José Hernández, Cristian Soto Jacome, Yaara Zisman‐Ilani, René H.J. Otten, Víctor M. Montori, Arwen H. Pieterse

Bibliographic record

VenuePatient Education and Counseling · 2024
Typereview
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversité LavalCentres Intégré Universitaires de Santé et de Services Sociaux
FundersKWF Kankerbestrijding
KeywordsClinical decision makingCancer treatmentMedical decision makingMEDLINEMedicineIntensive care medicineCancerManagement scienceFamily medicineInternal medicinePolitical scienceEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Time is often perceived as a barrier to shared decision making in cancer care. It remains unclear how time functions as a barrier and how it could be most effectively utilized. OBJECTIVE: This scoping review aimed to describe the role of time in patient involvement, and identify strategies to overcome time-related barriers. METHODS: Seven databases were searched for any publications on patient involvement in cancer treatment decisions, focusing on how time is used to involve patients, the association between time and patient involvement, and/or strategies to overcome time-related barriers. Reviewers worked independently and in duplicate to select publications and extract data. One coder thematically analyzed data, a second coder checked these analyses. RESULTS: The analysis of 26 eligible publications revealed four themes. Time was a resource 1) to process the diagnosis, 2) to obtain/process/consider information, 3) for patients and clinicians to spend together, and 4) for patient involvement in making decisions. DISCUSSION: Time is a resource throughout the treatment decision-making process, and generic strategies have been proposed to overcome time constraints. PRACTICE VALUE: Clinicians could co-create decision-making timelines with patients, spread decisions across several consultations, share written information with patients, and support healthcare redesigns that allocate the necessary time.

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: Review · Consensus signal: Review
Teacher disagreement score0.936
Threshold uncertainty score0.829

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.109
GPT teacher head0.477
Teacher spread0.368 · 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
GenreReview

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

Citations25
Published2024
Admission routes1
Has abstractyes

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