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Effectiveness of a Burn Prevention Campaign for Older Adults

2004· article· en· W2084069832 on OpenAlexaffabout
Jensen C. C. Tan, Carol Banez, Yvonne Cheung, Manuel Gómez, Nguyễn Văn Huy, Joanne Banfield, Ruth Lee, Robert Cartotto, Joel Fish

Bibliographic record

VenueJournal of Burn Care & Rehabilitation · 2004
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsSunnybrook Health Science CentreToronto Western HospitalHealth Sciences CentreUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineBathingBurn injuryFall preventionInjury preventionPopulationSuicide preventionOccupational safety and healthGerontologyPoison controlEnvironmental healthSurgery

Abstract

fetched live from OpenAlex

Older adults are involved in one fifth of burn injury admissions in the Province of Ontario Canada. Most burn injuries in this population occur at home while cooking, bathing, or smoking. The purpose of this study was to evaluate the effectiveness of an educational campaign to improve burn prevention knowledge in older adults of a major metropolitan city. Changes in participants' burn prevention knowledge were determined using standardized precampaign and postcampaign (4-6 weeks) surveys. Of 209 older adult participants, 126 (60.3%) completed the precampaign and postcampaign surveys. There was a significant increase (P <.05) in burn prevention knowledge postintervention. Age, education level, and living conditions did not influence the change in burn prevention knowledge. This burn prevention campaign for older adults was effective in improving burn prevention knowledge, but it remains unclear as to whether this will ultimately result in a change in burn prevention behavior.

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)0.002
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.299
Teacher spread0.292 · 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

Citations28
Published2004
Admission routes2
Has abstractyes

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