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Record W2481719255 · doi:10.1097/nur.0000000000000229

“One Flare at a Time”

2016· article· en· W2481719255 on OpenAlexaff
Olivia Skrastins, Paula C. Fletcher

Bibliographic record

VenueClinical Nurse Specialist · 2016
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsIrritable bowel syndromeCoping (psychology)PsychologyPleasureSalientDiseaseEveryday lifeLived experienceClinical psychologyMedicinePsychiatryPsychotherapistPathology

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this investigation is to study the lived experiences of female postsecondary students diagnosed with inflammatory bowel disease and/or irritable bowel syndrome. METHODS: Nine women between the ages of 18 and 26 years were recruited to participate in this study. Participants completed an informed consent form and background questionnaire before completing a semi-structured one-on-one interview. This interview explored the lived experiences of these individuals in relation to condition management. RESULTS: Three salient themes that emerged from the data included (1) it can add to my life; (2) why me: my condition runs my life; and (3) I'm doing the best I can with what I have. The salient theme of I'm doing the best I can with what I have, the theme addressed in this article, was subdivided into adaptive and maladaptive coping behaviors. Reasons for the use of these behaviors included to avoid triggers or flare-ups/harmful effects, to achieve instant relief/pleasure, to respond to environmental pressures, and to become accustomed to dealing with the everyday hassles of their conditions. CONCLUSION: Community health nurses may become part of the solution to help women with inflammatory bowel disease and irritable bowel syndrome find more adaptive coping behaviors. Other implications are discussed.

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)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.009

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 head0.053
GPT teacher head0.356
Teacher spread0.303 · 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; both teacher heads agree on what is shown here.

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

Citations12
Published2016
Admission routes1
Has abstractyes

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