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Record W2772525877 · doi:10.1097/nur.0000000000000345

“It Just Is What It Is”

2017· article· en· W2772525877 on OpenAlexaff
Olivia Skrastins, Paula C. Fletcher

Bibliographic record

VenueClinical Nurse Specialist · 2017
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

PURPOSE: This study aimed to explore the lived experiences of women diagnosed with inflammatory bowel disease and/or irritable bowel syndrome enrolled in postsecondary education. METHODS: Nine women aged 18 to 26 years participated in this study. Data collection consisted of an informed consent form, a background questionnaire, and a semistructured one-on-one interview. This interview explored the lived experiences of these individuals regarding perceived positive and negative effects of living with these conditions. RESULTS: Salient themes that emerged from the data were (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." Themes 1 and 2, the themes addressed in this article, were subdivided into (1) change in perception of self, condition, and others and (2) healthy lifestyle and (1) unpredictability and inconsistencies of inflammatory bowel disease/irritable bowel syndrome, (2) lack of understanding, and (3) the inconvenience of inflammatory bowel disease/irritable bowel syndrome, respectively. All participants expressed both positive and negative effects of living with their conditions. CONCLUSION: Community health nurses should be aware of the positive and negative effects of living with these conditions to help build relationships and assist with condition management. 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 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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.007
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0010.002
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 head0.188
GPT teacher head0.466
Teacher spread0.278 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations10
Published2017
Admission routes1
Has abstractyes

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