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Record W2003153904 · doi:10.5737/1181912x213140144

Evaluation of a workshop for survivors: Picking Up the Pieces

2011· article· en· W2003153904 on OpenAlexaffvenueabout
Margaret I. Fitch, Alison McAndrew, Sherri Magee, Fran Turner, Elisabeth Ross

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2011
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsOvarian Cancer CanadaSurgical Specialties (Canada)Occupational Cancer Research CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsCoping (psychology)Cancer survivorPsychologyMedicineMedical educationGerontologyCancerPsychotherapist

Abstract

fetched live from OpenAlex

As the cadre of cancer survivors grows, more information about the long-term impact of cancer and its treatment is becoming evident. Ovarian Cancer Canada (OCC) responded to identified needs of women who had been treated for ovarian cancer and developed a workshop program for survivors entitled, Picking Up the Pieces. This article describes the evaluation of the workshop, as it was offered to 170 survivors in eight sessions across Canada. The written surveys and in-depth interviews revealed the participants found the workshop very helpful in validating their experiences in coping as a survivor, connecting them with other survivors and a network of support, and providing practical tools to help them move forward to live the lives they envisioned. Cancer nurses are in ideal positions to encourage women to attend workshops designed for survivors. In addition, this program could serve as a model and be adapted for patients with other types 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 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)0.019
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0020.001
Open science0.0030.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.156
GPT teacher head0.386
Teacher spread0.230 · 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 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

Citations1
Published2011
Admission routes3
Has abstractyes

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