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The Systematic Development of a Nursing Intervention Aimed at Increasing Enrollment in Cardiac Rehabilitation for Acute Coronary Syndrome Patients

2009· article· en· W2077415637 on OpenAlexaff
Sylvie Cossette, L. D’Aoust, Magali Morin, Sonia Heppell, Nancy Frasure‐Smith

Bibliographic record

VenueProgress in Cardiovascular Nursing · 2009
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsMedicineAcute coronary syndromeRehabilitationIntervention (counseling)Intensive care medicineAnxietyPhysical therapyNursingInternal medicineMyocardial infarctionPsychiatry

Abstract

fetched live from OpenAlex

Acute coronary syndrome (ACS) is a leading cause of morbidity worldwide. Although cardiac rehabilitation (CR) programs can decrease recurrence of coronary events by as much as 25%, few patients engage in CR after a cardiac event. Current therapeutic procedures for ACS are provided quickly after the onset of symptoms, resulting in briefer hospital stays. Therefore, within this shorter time frame, the education of patients about ACS risk factors and their reduction presents a new nursing challenge. The purpose of this paper is to describe the systematic pathway in the development of a nursing intervention which addresses these new challenges in ACS risk factor reduction. The intervention aims to increase enrollment in CR, and enhance illness perceptions and medication adherence, while decreasing anxiety, risk factors, and emergency revisits.

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)0.008
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.345
Teacher spread0.330 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations9
Published2009
Admission routes1
Has abstractyes

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Same venueProgress in Cardiovascular NursingSame topicCardiac Health and Mental HealthFrench-language works237,207