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Record W2003036727 · doi:10.12968/gasn.2009.6.10.37852

Help-−seeking process in women with irritable bowel syndrome. Part 2: discussion

2009· article· en· W2003036727 on OpenAlexaff
Patricia Bourgault, Ghislain Devroede, Denise St-Cyr-Tribbl, Serge Marchand, Juliana Barcellos de Souza

Bibliographic record

VenueGastrointestinal Nursing · 2009
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsIrritable bowel syndromeMedicinePsychological interventionDistressPsychological distressProcess (computing)Qualitative researchEmotional distressMental healthNursingClinical psychologyAnxietyPsychiatry

Abstract

fetched live from OpenAlex

In part 1 of this 2−part study exploring the help−seeking process of women with irritable bowel syndrome (IBS), the authors presented the literature review, objectives, methodology and results. The reasons for seeking health care for IBS remain inadequately understood, and this process often seems complicated with poor outcomes. Thus, the aim of this study was to explore the entire help−seeking process of women with IBS. Specific objectives were to understand the roles of abdominal pain and psychological distress. A mixed method design was chosen to achieve the aim of the study: the qualitative part supports the help−seeking process′s theorization and the quantitative part serves to describe abdominal pain and psychological distress. The emergent model based on the interviews of nineteen women with IBS includes three categories: health problem, personal characteristics and help−seeking process. The last one represents the core of the process. In this second part, the authors′ provide a discussion of the results and propose future nursing interventions.

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)0.008
metaresearch head score (Gemma)0.012
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 score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0020.002
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 head0.011
GPT teacher head0.261
Teacher spread0.250 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

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