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Record W4226154649 · doi:10.1177/23743735221092633

Using the Delphi Method to Elucidate Patient and Caregiver Experiences of Cancer Care

2022· article· en· W4226154649 on OpenAlexafffund
Janet Ellis, Miriam von Mücke Similon, Melissa B. Korman, Sophia den Otter-Moore, Alva Murray, Kevin Higgins, Danny Enepekides, Marlene C. Jacobson

Bibliographic record

VenueJournal of Patient Experience · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsHealth Sciences CentreSunnybrook HospitalUniversity of TorontoSunnybrook Health Science Centre
FundersInstitute of Cancer ResearchSunnybrook Research Institute
KeywordsFocus groupDelphi methodPsychologyDelphiMedicineNursingSalient

Abstract

fetched live from OpenAlex

Objective: Identify the most salient elements of the head and neck cancer (HNC) care experience described by patients and caregivers in focus group interviews. Methods: Three focus groups of patients and caregivers were facilitated by research assistants and clinicians. Open-ended guiding questions captured/elicited aspects of care that were appreciated, warranted improvement, or enhanced communication and information. A four-step Delphi process derived consensus among focus group facilitators (n = 5) regarding salient discussion points from focus group conversations. Results: Seven salient themes were identified: (1) information provision, (2) burden related to symptoms and treatment side effects, (3) importance of social support, (4) quality of care at both hospital and provider levels, (5) caring for the person, not just treating cancer, (6) social and emotional impact of HNC, and (7) stigma and insufficient information regarding human papillomavirus-related HNC. Conclusion: Participants reported varying needs and support preferences, a desire for individualized communication, and to feel cared for as both a person and a patient. Findings illuminate the intricate details underlying high-quality, compassionate, person-centered HNC 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.228
GPT teacher head0.503
Teacher spread0.275 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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