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Record W2865173861 · doi:10.1111/nin.12250

Nursing activities for patients with chronic disease in family medicine groups: A multiple‐case study

2018· article· en· W2865173861 on OpenAlexaffabout
Marie‐Eve Poitras, Maud‐Christine Chouinard, Martin Fortin, Ariane Girard, Sue Crossman, Frances Gallagher

Bibliographic record

VenueNursing Inquiry · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsUniversité de SherbrookeCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsNursingMedicinePromotion (chess)Qualitative researchFamily medicine

Abstract

fetched live from OpenAlex

Family Medicine Groups (FMGs) are the most recently developed primary care organizations in Quebec (Canada). Nurses within FMGs play a central role for patients with chronic diseases (CD). However, this complex role and the nursing activities related to this role vary across FMGs. Inadequate knowledge of nursing activities limits the implementation of exemplary nursing practices. This study aimed to describe FMG nursing activities with patients with CD and to describe the facilitators and barriers to these activities. A multiple-case study was performed with ten nurses practicing among patients with CD in FMGs. Five data sources were used to provide an in-depth description of nursing activities and the facilitators and barriers to the development of these activities. After qualitative data analysis, findings show that nursing activities are clustered into five domains: Global assessment of the patient, Care management, Health promotion, Nurse-physician collaboration, and Planning services for patients with CD. Activities vary depending on contextual factors identified in each case. This multiple-case study provides a clear description of nursing activities with patients with CD. There is a need for improved nursing activities and expertise in domains of activities that are less present in FMGs, such as case management and interprofessional collaboration.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score1.000

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 head0.075
GPT teacher head0.379
Teacher spread0.304 · 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.

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

Citations40
Published2018
Admission routes2
Has abstractyes

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