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Record W2075411604 · doi:10.1097/sga.0b013e31829f3f9e

“It's a Big Part of Our Lives”

2013· article· en· W2075411604 on OpenAlexafffundabout
Paulien Brunings, Salman Klar, Gail Butt, Marjan D. Nijkamp, Jane A. Buxton

Bibliographic record

VenueGastroenterology Nursing · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSoutheast Asian Sociopolitical Studies
Canadian institutionsBC Centre for Disease Control
FundersPublic Health Agency of Canada
KeywordsMedicinePsychology

Abstract

fetched live from OpenAlex

Nurses play a key role in the ongoing treatment and management of chronic conditions such as Hepatitis C. Their skills in counseling, education, and as liaisons between patients, support services, and other healthcare providers make them crucial in the management of patients with Hepatitis C. Qualitative methods were used to explore and describe quality-of-care perspectives of patients receiving care in viral hepatitis clinics. Data were collected through focus group interviews at three hepatitis prevention and care demonstration projects located in underserved rural and small urban areas in British Columbia, Canada. Key themes were identified and used to construct a "Hepatitis C care model" and generate quality-of-care statements. These statements were then rated by another group of participants with Hepatitis C, using concept mapping. Most themes identified by the participants in focus groups (n = 21) related to care provision processes (autonomy, communication, education/information, continuity of care, professional competence, and support) rather than structure or outcomes of care. Concept-mapping participants (n = 20) rated communication as the key theme. Participants also highlighted the supportive role nurses played. Hepatitis C programming can be improved by leveraging nurses' strengths within multidisciplinary teams to address patient's concerns about process and communication issues.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.032
GPT teacher head0.317
Teacher spread0.286 · 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 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

Citations17
Published2013
Admission routes3
Has abstractyes

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