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Record W1980727066 · doi:10.1136/ip.2010.029215.347

A methodological template for the design and evaluation of evidence-based injury prevention fact sheets

2010· article· en· W1980727066 on OpenAlexaffabout
Michael Corbett, B. A. Morrongiello, Leora Ward

Bibliographic record

VenueInjury Prevention · 2010
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFocus groupProcess (computing)Poison controlInjury preventionVulnerability (computing)PsychologyHuman factors and ergonomicsSuicide preventionEvidence-based practicePopulationCognitionSocial cognitive theoryOccupational safety and healthApplied psychologyEngineeringMedicineSocial psychologyComputer securityComputer scienceMedical emergencyAlternative medicinePsychiatryEnvironmental healthBusinessMarketing

Abstract

fetched live from OpenAlex

In Canada, public health agencies often employ fact sheets or brochures as means of communicating injury prevention messaging to target groups; these are used because they are cost-effective and can reach large segments of the population. Unfortunately, the design of these instruments rarely reflects an evidence-based process, lacks theoretical grounding, and communication effectiveness is rarely assessed. In collaboration with Safe Kids Canada, we devised a methodological template for the development and evaluation of fact sheets targeting parental safety behaviours. This project involved the development of fact sheets targeting parents of infants at two ages (0–6, 6–12 months). The process employed a rigorous program evaluation framework, including a logic model, grounded in social cognitive theories and existing evidence that resulted in a focus on injury-prevention attitudes, self efficacy, and beliefs about vulnerability and severity of infant injury. Focus group testing with parents allowed us to compare the impact of alternative wording and images on cognitions, emotional reactions and parents injury-prevention beliefs and attitudes. Evidence drawn from these focus groups guided the final development of the fact sheets. A report was produced detailing evidence and recommendations as well as the process of developing and evaluating fact sheets, providing a template for future instrument development.

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)0.026
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.006
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)0.0010.000

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 head0.571
GPT teacher head0.534
Teacher spread0.037 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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 routes2
Has abstractyes

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