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Record W1507734713 · doi:10.24095/hpcdp.29.2.06

Complementary therapies for cancer patients: assessing information use and needs

2009· article· en· W1507734713 on OpenAlexaffvenueabout
Marja J. Verhoef, Lana S. Trojan, Gail Armitage, Linda E. Carlson, Robert J. Hilsden

Bibliographic record

VenueChronic diseases in Canada · 2009
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsAlberta Health ServicesUniversity of Calgary
FundersFondation pour la Recherche Médicale
KeywordsMedicineCancerHelplineScientific evidenceFamily medicineIntensive care medicineInternal medicineEmergency medicine

Abstract

fetched live from OpenAlex

Many cancer patients seek complementary therapies (CTs) for cancer management; however, relatively little is known about patients' CT information seeking behaviour. Therefore, we assessed: 1) cancer patients' use of the types and sources of CT information; 2) their information preferences; and 3) their understanding of the phrase "scientific evidence or proof that a therapy works." We collected data from 404 patients attending the Tom Baker Cancer Centre (TBCC) in Calgary and 303 patients calling the Cancer Information Service (CIS) helpline. In most cases, patients wanted information on the safety of CTs, how CTs work and their potential side effects. Physicians and conventional cancer centres were the most desired sources of CT information, but relatively few patients obtained information via these sources. Although patients were aware of the meaning of scientific evidence, they often used information based on non-scientific evidence, such as patient testimonials. The creation of a supportive care environment in conventional cancer treatment centres, by providing CT information, may help address cancer patients' concerns and alleviate some of the stress that may have been caused by the cancer diagnosis.

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 score0.171
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.026
GPT teacher head0.314
Teacher spread0.287 · 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

Citations54
Published2009
Admission routes3
Has abstractyes

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