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Record W3017299271 · doi:10.1002/nop2.477

A qualitative study exploring the influence of clinic funding on the integration of family practice nurses in Newfoundland and Labrador

2020· article· en· W3017299271 on OpenAlexaffabout
Maria Mathews, Dana Ryan, Richard Buote, Sandra Parsons, Julia Lukewich

Bibliographic record

VenueNursing Open · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsGovernment of Newfoundland and LabradorMemorial University of NewfoundlandWestern University
Fundersnot available
KeywordsPaymentNursingService (business)Work (physics)Qualitative researchFocus groupPrimary carePayment by ResultsMedicineFamily medicineBusinessSociologyMarketingPolitical science

Abstract

fetched live from OpenAlex

AIM: This study explores the contributions of family practice nurses in primary care across Newfoundland and Labrador funded by fee-for-service and alternate payment plans to examine the influence of funding arrangements on nursing roles/activities. DESIGN: A qualitative descriptive design was employed. METHODS: Semi-structured telephone interviews were conducted between March-July 2018 with physicians and Registered Nurses working in primary care settings in Newfoundland and Labrador. Interviews were transcribed verbatim, and a content analysis approach was used to identify recurring themes. RESULTS: Clinic funding was instrumental in the integration of family practice nurses into primary care settings and influenced roles/activities. In fee-for-service practices, nurses work with physicians and focus on one-on-one patient care in office-based settings, whereas nurses in alternate payment plans practices work more independently, in a wider range of settings and with emphasis on both individual and group-based encounters. Compared with alternate payment plans practices, fee-for-service practices tend to be more restrictive due to physician billing requirements.

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)0.006
metaresearch head score (Gemma)0.008
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 score0.589
Threshold uncertainty score0.816

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.006
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.475
GPT teacher head0.595
Teacher spread0.120 · 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

Citations14
Published2020
Admission routes2
Has abstractyes

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