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Who visits mobile UK services providing cancer information and support in the community?

2009· article· en· W1975659261 on OpenAlexaboutno aff
Claire Foster, Ian Scott, Julia Addington‐Hall

Bibliographic record

VenueEuropean Journal of Cancer Care · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsnot available
FundersMacmillan Cancer Support
KeywordsMedicineVisitor patternConversationCancerQuarter (Canadian coin)Variety (cybernetics)Family medicineNursingInternet privacy

Abstract

fetched live from OpenAlex

People can access a variety of sources of information and support when they have questions about cancer according to their needs. There are various sources of information and support for cancer beyond the health-care setting. In this study, we set out to assess reasons for visiting two mobile cancer information and support services in the UK during 2006. Data were collected about each visitor by staff on the mobile services. The two mobiles travelled to 109 UK locations over a 7-month period. Fifty-nine per cent of visitors were women. Thirty-one per cent of visitors had (had) cancer; very few were still undergoing treatment. For 95% of visitors the visit had been spontaneous rather than pre-planned, and 89% of visits lasted <15 min. Most visitors required information or support for themselves, but a third requested information for someone else. A quarter of enquiries were about cancer prevention and early detection (e.g. screening, genetic testing, lifestyle). The mobiles appear to serve an important function in providing information and support in the community where visitors can drop in for an informal conversation with trained members of staff to ask questions and receive support in relation to cancer.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.016
GPT teacher head0.308
Teacher spread0.292 · 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 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

Citations8
Published2009
Admission routes1
Has abstractyes

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