MétaCan
Menu
Back to cohort
Record W2165844323 · doi:10.3747/co.v15i0.270

Supporting Cancer Patients through the Continuum of Care: A View from the Age of Social Networks and Computer-Mediated Communication

2008· article· en· W2165844323 on OpenAlexaffvenue
Jacqueline L. Bender, Laura O’Grady, Alejandro R. Jadad

Bibliographic record

VenueCurrent Oncology · 2008
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsJuravinski Cancer CentrePublic Health OntarioUniversity Health Network
Fundersnot available
KeywordsMedicineContinuum of careCancerHealth careInternal medicine

Abstract

fetched live from OpenAlex

Almost since its inception, the Internet has been used by ordinary people to connect with peers and to exchange health-related information and support. With the rapid development of software applications deliberately designed to facilitate social interaction, a new era is dawning in which patients and their loved ones can collaboratively build knowledge related to coping with illness, while meeting their mutual supportive care needs in a timely way, regardless of location. In this article, we provide background information on the use of "one-to-one" (for example, e-mail), "one-to-many" (for example, e-mail lists), and "many-to-many" (for example, message boards and chat rooms, and more recently, applications associated with Web 2.0) computer-mediated communication to nurture health-related social networks and online supportive care. We also discuss research that has investigated the use of social networks by patients, highlight opportunities for health professionals in this area, and describe new advances that are fuelling this new era of collaboration in the management of 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)0.001
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 score0.351
Threshold uncertainty score0.602

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.142
GPT teacher head0.519
Teacher spread0.377 · 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

Citations42
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueCurrent OncologySame topicHealth Literacy and Information AccessibilityFrench-language works237,207