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A Comparison of the Information Needs of Women Newly Diagnosed With Breast Cancer in Malaysia and the United Kingdom

2005· article· en· W2162103679 on OpenAlexaboutno aff
Raja Gopal, Kinta Beaver, Tony Barnett, Nik Safiah Nik Ismail

Bibliographic record

VenueCancer Nursing · 2005
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
FundersNational Institute for Health and Care Research
KeywordsMedicinePsychosocialAttractivenessBreast cancerInformation needsFamily medicineDiversity (politics)Health careGerontologyCancerNursingPsychologyPsychiatry

Abstract

fetched live from OpenAlex

Little is known about the information needs of women with breast cancer in non-Western societies. This study examined the priority information needs of 100 women with breast cancer in Malaysia and compared the findings to previous work involving 150 women diagnosed with breast cancer in the United Kingdom. The study used a valid and reliable measure, the Information Needs Questionnaire (INQ). The INQ contained 9 items of information related to physical, psychological, and social care, used successfully in Canada and the United Kingdom. The INQ was shown to have cross-cultural relevance and sensitivity. For Malaysian women, information about likelihood of cure, sexual attractiveness, and spread of disease were the most important information needs. For UK women, similar priorities were evident, apart from the item on sexual attractiveness, which was ranked much lower by women in the United Kingdom. The cultural similarities and differences that emerged from this study have implications for nurses in the cancer field caring for people from a diversity of cultural backgrounds. Breast care nurses are not a feature of the Malaysian healthcare system, although the findings from this study support the view that specialist nurses have a vital role to play in meeting the psychosocial needs of women with breast cancer in non-Western societies.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.994

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 studies0.0000.000
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 head0.103
GPT teacher head0.423
Teacher spread0.319 · 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

Citations35
Published2005
Admission routes1
Has abstractyes

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