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Record W2744326576 · doi:10.5737/23688076273236242

Fear of cancer recurrence: A study of the experience of survivors of ovarian cancer

2017· article· en· W2744326576 on OpenAlexaffvenue
Jamie Kyriacou, Alex Black, Nancy Drummond, Joanne Power, Christine Maheu

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsMcGill UniversityJewish General HospitalMcGill University Health Centre
Fundersnot available
KeywordsWorryPsychosocialOvarian cancerCoping (psychology)Fallopian tubeSurvivorship curveQualitative researchCancer recurrenceMedicineGynecologyOncologyPsychologyInternal medicineClinical psychologyCancerObstetricsPsychotherapistAnxietyPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study is to better understand fear of cancer (FCR) through the experience of ovarian and fallopian tube cancer survivors. METHODS: This study used a descriptive qualitative design. Twelve participants in remission from ovarian or fallopian tube cancer were recruited. Researchers conducted face-to-face, semi-structured interviews and the content, transcribed verbatim, underwent content analysis. RESULTS: FCR has been identified as a significant concern for women in remission from ovarian cancer. Four themes emerged from the participants' FCR experience: (a) uncertainty surrounding recurrence; (b) varied beliefs and sources of worry; (c) perceived risk of recurrence; (d) management of FCR. IMPLICATIONS: Survivorship support can be optimized by nurses by screening for FCR, offering psychosocial support for women at risk for FCR, teaching and reinforcing adaptive coping strategies.

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)0.004
metaresearch head score (Gemma)0.013
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 score0.019
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.386
Teacher spread0.344 · 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

Citations29
Published2017
Admission routes2
Has abstractyes

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