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Record W2767487868 · doi:10.28984/drhj.v1i0.28

Impact of Advanced Access Scheduling on Patient Care Choices and Health Behaviours in a Nurse Practitioner-Led Clinic

2017· article· en· W2767487868 on OpenAlexaffvenueabout
Roberta Heale, Jennifer-Lynn Fournier

Bibliographic record

VenueDiversity of Research in Health Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsLaurentian University
Fundersnot available
KeywordsHealth careMedicineNurse practitionersNursingFamily medicine

Abstract

fetched live from OpenAlex

The Nurse Practitioner-Led Clinic (NPLC) is a new model of primary healthcare. The wholistic approach of nurse practitioner (NP) led care in an NPLC that implements Advanced Access scheduling has the potential to enhance timely access to care and improve health outcomes. The purpose of this study was to determine the experience of patients in one NPLC as well as their healthcare behaviours related to Advanced Access scheduling. A previously developed survey with items related to appointment access, health behaviours and satisfaction was mailed once to patients at a NPLC in northern Ontario. 535 patients replied for a response rate of 29%. A majority (85.4%) were able to access same-day appointments. Access to same-day appointments was associated with less likelihood of attending a walk-in-clinic or emergency department in addition to self-reports of improvements in lifestyle and better control of medical condition(s). Advanced access scheduling contributes to optimal patient care in an NPLC setting. The NP role in lifestyle counselling and wholistic care in the NPLC model contributes to improved self-reported health. Access to an appointment at a point of ‘readiness’ may positively contribute to lifestyle changes and overall health of patients.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.260
GPT teacher head0.574
Teacher spread0.314 · 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 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

Citations1
Published2017
Admission routes3
Has abstractyes

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