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Record W2395589551

The Influence of Causal Knowledge on the Comprehension and Retention of Medical Information among Younger and Older Adults

2014· article· en· W2395589551 on OpenAlexfundno aff
Karen Michelle Zhang, Leora C. Swartzman, John Paul Minda

Bibliographic record

VenueeScholarship (California Digital Library) · 2014
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsForgettingTest (biology)ComprehensionPsychologyKnowledge retentionHealth literacyDevelopmental psychologyGerontologyCognitive psychologyMedicineMedical educationHealth careComputer science
DOInot available

Abstract

fetched live from OpenAlex

Older adults are often susceptible to confusing or forgetting medical instructions.The purpose of the present study was to examine the effects of causal knowledge on the learning and retention of medical information among younger and older adults.Participants were asked to read about a fictitious disease with or without explanations on the cause-and -effects of illness management.A multiple-choice knowledge test was administered immediately and 1-week following the presentation of health booklets.Results demonstrated that causal knowledge facilitated the application and retention of novel medical knowledge across time for younger adults.In contrast, causal explanations did not seem to influence the test performances of older participants.After controlling for age, verbal ability, working memory, and health literacy, provision of causal explanation explained a significant amount of unique variance in test performance.Incorporating causal explanations in health education materials may have the potential to help patients acquire medical knowledge.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.292

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 communication0.0000.001
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 head0.220
Teacher spread0.212 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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