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Cancer disclosure: Experiences of Iranian cancer patients

2012· article· en· W1585435561 on OpenAlexaff
Leila Valizadeh, Vahid Zamanzadeh, Azad Rahmani, A. Fuchsia Howard, Alireza Nikanfar, Caleb Ferguson

Bibliographic record

VenueNursing and Health Sciences · 2012
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversity of British Columbia
FundersUniversity of TabrizNorthwestern University
KeywordsCredibilityTransferabilityDistressQualitative researchMedicineContent analysisSelf-disclosurePsychologyFamily medicineClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

This study explored Iranian patients' experiences of cancer disclosure, paying particular attention to the ways of disclosure. Twenty cancer patients were invited to participate in this qualitative inquiry by research staff in the clinical setting. In-depth, semistructured interview data were analyzed through content analysis. The rigor of the study was established by principles of credibility, transferability, dependability, and confirmability. Four themes emerged: the atmosphere of non-disclosure, eventual disclosure, distress in knowing, and the desire for information. Non-disclosure was the norm for participants, and all individuals involved made efforts to maintain an atmosphere of non-disclosure. While a select few were informed of their diagnosis by a physician or another patient, the majority eventually became aware of their diagnosis indirectly by different ways. All participants experienced distress after disclosure. The participants wanted basic information about their prognosis and treatments from their treating physicians, but did not receive this information, and encountered difficulty accessing information elsewhere. These challenges highlight the need for changes in current medical practice in Iran, as well as patient and healthcare provider education.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.334
GPT teacher head0.528
Teacher spread0.193 · 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

Citations50
Published2012
Admission routes1
Has abstractyes

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