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Record W4233743860 · doi:10.12927/hcq.2003.16765

Adversaria

2003· article· en· W4233743860 on OpenAlexaff
Peggy Leatt

Bibliographic record

VenueHealthcare Quarterly · 2003
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Science and Medicine
Canadian institutionsUniversity of TorontoInstitute for Work & Health
Fundersnot available
KeywordsBusinessMedicineNursing

Abstract

fetched live from OpenAlex

paper, "Prevention: Delivering the Goods," with its focus on prevention, is very timely as current developments in infectious diseases, in particular SARS, bring the field of public health to the forefront.Excellence in public health practices is easily overlooked or taken for grantedyet, safe water, air, food and other environmental factors are essential to our survival.Frank and Di Ruggiero ask why prevention has been so slow to have a major effect.The authors are eminently qualified to answer such a question because of their wealth of experience and education in disease prevention and health promotion.As they point out, it is not exactly clear what is meant by "good" preventive practice.We all know from personal experiences how confusing expert opinion can be.For example, if we want to reduce our probability or risk of heart failure we are advised to do a combination of the following: eat healthily (which can be interpreted in many ways -lower fat intake, no red meat, eat lots of fruit and vegetables), lose weight, exercise regularly (varying advice about how much and what type), take aspirin daily and/or a wide variety of other dietary supplements.Add these probabilities together and follow the advice, we should all live to over 100, but we all know that is unlikely.Frank and Di Ruggiero attribute the lack of progress in prevention to our "premature enthusiasm" for new approaches and the constant search for a magic bullet.In terms of healthy behaviours, we may all have good intentions, but unfortunately we let things slide.The authors remind us how challenging it is to carry out simple preventive practices such as forgetting to floss or brush our teeth..Even more difficult is trying to change a behaviour such as smoking, even though the evidence against it is clear.Prevention may be better than cure, but we are all vulnerable to effects of genetics and social circumstances.Frank and Di Ruggiero stress the importance of opening up a public dialogue if we want large-scale prevention to work.They make a case for multiple interventions, at both the individual and community levels, to make the culture more receptive to change.We all have a great deal to learn about how to make the case for prevention stick.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.947
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.039
GPT teacher head0.270
Teacher spread0.231 · 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 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
Published2003
Admission routes1
Has abstractyes

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