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Record W2004916019 · doi:10.1136/ip.2003.003327

Child safety education and the world wide web: an evaluation of the content and quality of online resources: Table 1

2004· article· en· W2004916019 on OpenAlexaff
Debra Isaac, Michael D. Cusimano, Alexander Sherman, Mary L. Chipman

Bibliographic record

VenueInjury Prevention · 2004
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsThe InternetQuality (philosophy)Resource (disambiguation)Web resourceScale (ratio)World Wide WebTable (database)EngineeringComputer scienceDatabaseGeography

Abstract

fetched live from OpenAlex

The purpose of this study was to assess the content, quality, and type of internet resources available for safety education. Using 19 search engines with search strings targeting major forms of injury, identified resources were classified by audience group, accessibility, and authorship. Two independent reviewers rated each resource on the basis of its content and a set of quality criteria using a three point scale. Overall, 10 (18.2%) resources were of highest quality, four (7.3%) were intermediate, and 41 (74.5%) were not recommended. Eighteen months after the original search, 67.3% of all resources and 90% of the highest quality resources were still on the internet. This study provides a methodology for evaluating child safety resources on the world wide web and demonstrates that most internet resources for safety education are of dubious quality. A rating system such as the one developed for this study may be used to identify valuable internet materials.

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)0.009
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.060
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.006
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.120
GPT teacher head0.495
Teacher spread0.375 · 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.

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

Citations20
Published2004
Admission routes1
Has abstractyes

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