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Record W2781435938 · doi:10.32378/ijp.v2i2.67

Building research capacity among Irish prehospital practitioners.

2017· article· en· W2781435938 on OpenAlexaff
Alan M Batt, Shane Knox

Bibliographic record

VenueIrish Journal of Paramedicine · 2017
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsFanshawe College
Fundersnot available
KeywordsIrishPromotion (chess)Context (archaeology)MedicinePublic relationsPolitical science

Abstract

fetched live from OpenAlex

The National Prehospital Research Strategy was published in 2008, which aimed to gauge current levels of prehospital research activity in Ireland; ascertain the research strengths that exist in the Irish prehospital community; pinpoint the obstacles to high quality research in the prehospital arena; determine the building blocks for a national prehospital research culture; outline an implementation plan for the strategy; and, identify expected and measurable short and long-term outcomes of implementing the strategy. The characteristics of systems that facilitate research productivity include: the promotion of a research culture; mentoring by established scholars; communication with a professional network; rewards for research; and brokered opportunities. Almost ten years later, the development of research capacity within the practitioner cohort has not progressed as much as hoped. A culture of research provides a supportive context in which research is uniformly expected, discussed, produced, and valued. We propose that a culture of research does not yet exist among Irish prehospital practitioners.

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)0.097
metaresearch head score (Gemma)0.129
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score0.514

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0970.129
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0070.011
Scholarly communication0.0130.009
Open science0.0030.017
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0090.002

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 head0.192
GPT teacher head0.459
Teacher spread0.267 · 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.

Study designNot applicable
DomainMethods
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

Citations6
Published2017
Admission routes1
Has abstractyes

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