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Record W2167157592 · doi:10.1002/pon.590

Providing internet lessons to oncology patients and family members: a shared project

2002· article· en· W2167157592 on OpenAlexaff
Linda Edgar, Arlene Greenberg, Jean Remmer

Bibliographic record

VenuePsycho-Oncology · 2002
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsThe InternetIntervention (counseling)Session (web analytics)Medical educationMedicineService (business)Internet accessFamily medicinePsychologyNursingWorld Wide WebComputer scienceBusiness

Abstract

fetched live from OpenAlex

The paper describes the steps in a pilot study taken to develop and evaluate an Internet intervention for cancer patients and family members. The intervention was a shared project of two hospital departments; a volunteer oncology support service, Hope & Cope, and the Health Services Library. Forty subjects were surveyed on their computer use and interest, and of these, half used the Internet to access health and medical information. Of the 40 subjects, 28 participated in an innovative, one-to-one teaching session with a medical librarian where they learned to access Internet sites to find information specific to their needs and subsequently be more confident in their perceived ability to evaluate the information received. Follow up interviews found that the sessions were well received and at two months follow-up the participants attributed their positive well being in large part to the intervention. Internet use by oncology patients and family members in conjunction with skilled help has the potential to contribute in a timely fashion to the well being of those with 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0060.003
Scholarly communication0.0020.002
Open science0.0020.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.193
GPT teacher head0.520
Teacher spread0.328 · 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 source (direct Gemma or distilled Codex), 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

Citations31
Published2002
Admission routes1
Has abstractyes

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