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Record W2067771393 · doi:10.5737/1181912x2111620

Survivors Teaching Students: Increasing awareness about ovarian cancer

2011· article· en· W2067771393 on OpenAlexaffvenueabout
Margaret I. Fitch, Alison McAndrew, F. Darell Turner, Elisabeth Ross, Iris Pison

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2011
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsOvarian Cancer CanadaSunnybrook Health Science Centre
Fundersnot available
KeywordsOvarian cancerSession (web analytics)MedicineMedical educationDiseaseOncologyCancerFamily medicinePsychologyInternal medicineComputer science

Abstract

fetched live from OpenAlex

New evidence has emerged concerning the persistent presence of a combination of symptoms that can be indicative of ovarian cancer. Professionals need to be aware of this evidence and incorporate it into their practice. Ovarian Cancer Canada (OCC) has implemented an innovative program in selected Canadian universities, as a method to educate undergraduate medical and nursing students about ovarian cancer. Survivors Teaching Students has been offered to 3,620 undergraduate students. This article presents the evaluation of the program using pre and post session surveys. Overall, students reported a change in knowledge related to the progression of ovarian cancer, symptoms of ovarian cancer, risk factors for the disease, and perspectives about what a woman with ovarian cancer might feel on being diagnosed. Having survivors provide the classroom session brought "a face and a voice" to the issue of ovarian cancer for the students.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.049
GPT teacher head0.372
Teacher spread0.323 · 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 designNot applicable
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

Citations14
Published2011
Admission routes3
Has abstractyes

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