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Record W4402316453 · doi:10.54536/ajmsi.v3i2.2935

An Outcomes Comparison between Nurse Practitioners and Primary Care Physicians in Quality of Life in Older Patients with Congestive Heart Failure

2024· article· en· W4402316453 on OpenAlexaboutno aff
Mohammad I D Ibrahim, Shannon, McCrory Churchill

Bibliographic record

VenueAmerican Journal of Medical Science and Innovation · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsnot available
Fundersnot available
KeywordsHeart failureMedicinePrimary careQuality of life (healthcare)NursingIntensive care medicineQuality (philosophy)Family medicineCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Chronic heart failure (CHF) is a prevalent cardiovascular disease affecting patients' outcomes and quality of life. Nurse Practitioners (NPs) and Primary Care Physicians (PCPs) are renowned for their positive impact on patient satisfaction and quality of life. However, the extent to which they achieve these results still needs to be explored. The study aimed to determine if the disease course of older CHF patients under NPs and PCPs remained consistent, focusing on patients' satisfaction levels in NP care compared to those in PCP care in primary care settings. A comparative observational design was used to recruit CHF patients aged 65 and above from nursing homes in Ontario, Canada. Subjects completed the 12-question questionnaire to gauge satisfaction and overall quality of life. Results were analysed using ANOVA as outcome indicators of the NPs and PCPs were to be compared.Findings showed no significant variation in quality of life score measurement between NP and PCP patients. Both (NPs and PCPs) were revealed to be equally strong in meeting the demanding CHF patients' needs. The study emphasises the crucial role of Nurse Practitioners (NPs) in multidisciplinary Team CHF care, highlighting their role in improving patient outcomes and healthcare delivery. It acknowledges the limitations of the measurement study, such as sample size, and contributes to ongoing debates on healthcare delivery.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.063
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.050
GPT teacher head0.492
Teacher spread0.442 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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