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Record W2086224389 · doi:10.1080/07399330601128445

Survivor Dragon Boating: A Vehicle to Reclaim and Enhance Life After Treatment for Breast Cancer

2007· article· en· W2086224389 on OpenAlexaff
Terry Mitchell, Christine Yakiwchuk, Kara Griffin, Ross Gray, Margaret I. Fitch

Bibliographic record

VenueHealth Care For Women International · 2007
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsSunnybrook Health Science CentreCancer Care OntarioWilfrid Laurier University
Fundersnot available
KeywordsPsychosocialBreast cancerQuality of life (healthcare)Qualitative researchPsychologyGerontologyCancerMedicineSociologyPsychiatryPsychotherapistSocial science

Abstract

fetched live from OpenAlex

The authors investigated the psychosocial impact of dragon boat participation on women who have been treated for breast cancer. Open-ended qualitative interviews were completed by 10 new members recruited from two breast cancer survivor dragon boat teams. Our findings indicate that the women's experience of survivor dragon boating surpassed their expectations and offered them hope and increased strength and the ability to regain control of their lives. Key themes emerging from the in-depth interviews that encapsulate the experiences of women in their first season of dragon boating follow: awakening of the self, common bond, regaining control, being uplifted, and transcending the fear of death. The interview data support the emerging hypothesis that dragon boating is a vehicle for improving women's wellness and post-treatment quality of life.

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)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.364
Teacher spread0.348 · 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

Citations42
Published2007
Admission routes1
Has abstractyes

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