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Record W2057929790 · doi:10.1188/12.cjon.38-41

Autoethnography: Reflective Journaling and Meditation to Cope With Life-Threatening Breast Cancer

2012· article· en· W2057929790 on OpenAlexaff
Patricia A. Sealy

Bibliographic record

VenueClinical journal of oncology nursing · 2012
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsJournaling file systemAutoethnographyMeditationPsychotherapistGriefQualitative researchBreast cancerPsychologyContext (archaeology)MedicineCancerSociology

Abstract

fetched live from OpenAlex

Autoethnography is a qualitative research approach whereby the researcher shares personal stories that relate to the broader cultural context. Living through breast cancer showed me how reflective journaling and meditation can help one to cope with locally advanced breast cancer. The purpose of this autoethnography is to assist nurses in gaining a greater understanding of the primary cultural implications of (a) unresolved emotional issues from the past complicating current treatment and recovery for locally advanced breast cancer, and that (b) reflective journaling and meditation can provide an opportunity to "socially reconstruct" past psychological injury. In this example of autoethnography, I reconstructed the past by re-experiencing childhood wounds through meditation, accompanied by myself in the role of the nurturing mother providing comfort and support to the wounded inner child. That approach affirmed me in my current mothering role and provided imagery of the comfort that I was lacking in my childhood. Such duality empowered me toward self-acceptance and self-worth. Loss, grief, fear, and anxiety are considered universal states and emotions that interfere with quality of life. Finding meaning in suffering can heal pain and free energy for the pursuit of justice, peace, and joy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.713
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.157
GPT teacher head0.549
Teacher spread0.392 · 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 teacher head, 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

Citations27
Published2012
Admission routes1
Has abstractyes

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