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Record W6977076704 · doi:10.60692/cpe3n-5ch86

Barriers to communicating a cancer diagnosis to patients in a low- to middle-income context

2021· article· en· W6977076704 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversité de MontréalMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsThematic analysisFocus groupContext (archaeology)Health careHealth communicationHealth professionalsStigma (botany)Communication skills training

Abstract

fetched live from OpenAlex

Abstract Objective. The aim of this study was to understand the needs and experiences of oncology professionals involved in communicating a diagnosis to adult and pediatric cancer patients in the low-middle income (LMI) context of Kenya, with a focus on identifying barriers and facilitators. Methods. A World Café focus group methodology was conducted and comprised 19 discussion groups of approximately 6 participants (n = 114 professionals). Thematic analysis was used to clarify barriers and facilitators of professional–patient communication. Results. Participants reported several obstacles that hinder communication between cancer patients and healthcare professionals in Kenya, including: patient-related barriers (ie, lack of terminology, health literacy), culture-related barriers (ie, cultural and religious beliefs about cancer, beliefs about children), and physician- and system-related barriers (ie, limited communication skills, organizational barriers). Communication facilitators included: the central role of family and cultural traditions, and the caring attitude of physicians when disclosing diagnosis and treatment procedures. Conclusions. The data indicate the potential need to adapt communication guidelines to the Kenyan context to disseminate skills training in a culturally relevant way. Also of importance may be to embed parallel public health strategies and health care structural changes to facilitate disclosure and mitigate any unintended negative effects such as stigma and social isolation.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.003

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 head0.167
GPT teacher head0.357
Teacher spread0.190 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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