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Record W2013883692 · doi:10.12968/bjca.2009.4.8.43491

Past use of and current satisfaction with a nurse-led hospital cardiac helpline

2009· article· en· W2013883692 on OpenAlexaboutno aff
Andy McEwen, Lis Billings

Bibliographic record

VenueBritish Journal of Cardiac Nursing · 2009
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsnot available
Fundersnot available
KeywordsHelplineMedicineQuarter (Canadian coin)Medical emergencyPopulationFamily medicineEmergency departmentNursingEmergency medicineEnvironmental health

Abstract

fetched live from OpenAlex

Objectives: To review the use of a nurse-led cardiac helpline since its inception, and to assess the current satisfaction of recent callers to the helpline. Design and setting: The study took place in a cardiac unit that provides a service to an inner-city London population, but is also a tertiary centre providing highly specialized services to a wider population. Call records to the cardiac helpline between 1996 and 2004 (n=8429) were analysed. Additionally, a postal satisfaction survey was sent to the last 100 callers to the helpline in 2005 (response rate=79%). Results: At its peak the cardiac helpline was called on average more than four times a day, but was still called at least once a day in 2004. Over half (54%) of calls were about a physical complaint and 15% concerned medication. In more than one-quarter (29%) of cases the caller was advised to contact his/her GP and 8% were directed to attend accident and emergency (A&E). Surveyed callers to the cardiac helpline were highly satisfied with the service and 39% of respondents said that without the helpline they would have visited their GP while 29% would have attended A&E. Conclusions: This hospital-based, nurse-led cardiac helpline achieved a high degree of satisfaction with callers and offers patients an instant access alternative to consulting with their GP or attending their local A&E department.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.985
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.018
GPT teacher head0.339
Teacher spread0.321 · 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

Citations7
Published2009
Admission routes1
Has abstractyes

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