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Record W2898413497 · doi:10.1177/1471301217731385

The Kids4Dementia education program is effective in improving children’s attitudes towards dementia

2018· article· en· W2898413497 on OpenAlexfundno aff
Jess Baker, Belinda Goodenough, Yun‐Hee Jeon, Christine Bryden, Karen Hutchinson, Lee‐Fay Low

Bibliographic record

VenueDementia · 2018
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
FundersDementia Collaborative Research Centres, AustraliaAlzheimer Society
KeywordsDementiaPsychologyCurriculumEmpathyStigma (botany)Medical educationDevelopmental psychologyClinical psychologyMedicinePedagogyPsychiatryDisease

Abstract

fetched live from OpenAlex

Improving children’s understanding of people with dementia is essential for tackling societal stigma around dementia. Kids4Dementia is a teacher-led multimedia dementia education resource for 9–12 year olds (approximately 150 minutes duration). A non-randomised, waitlist-controlled, mixed-methods design examined whether Kids4Dementia was (1) efficacious in improving students” attitudes towards people with dementia and (2) engaging and acceptable for teachers and students. Students who completed Kids4Dementia (n = 136) showed improved scores on the Kids Insight into Dementia Survey, relative to the control school (n = 67), especially students who had not heard of dementia before (Time × Group × Dementia Familiarity interaction, F(1, 191) = 5.28, p = .023, partial η 2 = .027). Qualitative reports indicated that the program was acceptable and engaging for teachers and students and corroborated improvement in student empathy and behavioural intentions towards people with dementia. The findings provide preliminary evidence for the efficacy of Kids4Dementia as an engaging, stakeholder-directed, curriculum-aligned dementia education program.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.518

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.325
Teacher spread0.318 · 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 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
Published2018
Admission routes1
Has abstractyes

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