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Record W2038281990 · doi:10.1136/ip.2010.029215.579

Making good samaritans skilled Samaritans

2010· article· en· W2038281990 on OpenAlexaboutno aff
Ronald G. Evens

Bibliographic record

VenueInjury Prevention · 2010
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
Fundersnot available
KeywordsFirst aidLegislatureQuarter (Canadian coin)PopulationNothingLawPublic relationsMedical emergencyPolitical sciencePsychologyBusinessMedicineHistoryEnvironmental health

Abstract

fetched live from OpenAlex

Many countries around the world have what is known as a Good Samaritan Law, protecting the rights of those citizens that go out of their way to help those in need, most commonly in emergency situations. This protection may well be a step in the right direction, but does not address the fundamental issue of first aid knowledge. If more people were trained in this life-saving skill, businesses and the general public would have greater confidence in knowing that valuable help was at hand. Up to 150 000 people die each year in situations where first aid may have made the difference – four times the number who die each year from the biggest cause of cancer or the equivalent of the entire population of Basingstoke. Our research found that almost two thirds of the general public wouldnt feel confident in administering first aid in an emergency. Even more disturbingly, a quarter would stand by and do nothing, hoping that someone else knows first aid. In spite of these alarming statistics, the UK like many other countries is still guilty of treating first aid as a legislative requirement, rather than a necessary life skill. In communities where medical services might not be easily available, a little first aid knowledge could go a long way. Richard Evens, commercial training director, St John Ambulance, will discuss the importance of first aid knowledge for citizens of all ages, giving examples of where these skills have made a difference between life and death.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.061
GPT teacher head0.468
Teacher spread0.407 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2010
Admission routes1
Has abstractyes

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