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Record W2504920334 · doi:10.17225/jhp00078

Haemophilia nursing practice: A global survey of roles and responsibilities

2016· article· en· W2504920334 on OpenAlexaff
Kate Khair, Mahmoud Abu-Riash, Ana Cláudia Acerbi, M. Beijlevelt, Georgina Floros, Kuixing Li, Ljiljana Rakić, Bongi Mbele, Robyn Shoemark, Jim Munn

Bibliographic record

VenueThe Journal of Haemophilia Practice · 2016
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsHaemophiliaNursingMedicineAttendanceWork (physics)Best practiceMedical educationFamily medicinePolitical sciencePediatrics

Abstract

fetched live from OpenAlex

Abstract Haemophilia nursing roles continue to develop alongside nursing as a profession. There are now nurses who practice autonomously, much like a medical practitioner, and many who have extended their roles to deliver direct patient care, education and research. There has been little, if any, comparison with haemophilia nurse roles internationally, nor of the impact of these roles on patient reported outcomes. This paper reports the results of an international survey, of 297 haemophilia nurses from 22 countries, describing current day practice and care. Many nurses work above and beyond their funded hours to improve care through research and evidence-based practice. While some are able to attend international meetings to report and discover this evidence, many due to financial constraints, are not. Others reported difficulty with communicating in English, which limited congress attendance. With on-line learning capability, sharing of best practice is now possible, and this approach should be a platform developed in coming years to further enhance haemophilia nursing practice and ultimately patient care.

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)0.010
metaresearch head score (Gemma)0.035
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.051
GPT teacher head0.397
Teacher spread0.346 · 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.

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

Citations4
Published2016
Admission routes1
Has abstractyes

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