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Record W2096753587 · doi:10.3122/jabfm.2008.03.070217

How Long Does It Take to Assess Literacy Skills in Clinical Practice?

2008· article· en· W2096753587 on OpenAlex
KATE JOHNSON, B. D. Weiss

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of the American Board of Family Medicine · 2008
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsCentre for Family Medicine
FundersPfizer
KeywordsMedicineHealth literacyPrimary careStopwatchLiteracyConfidence intervalFamily medicineHealth careInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Health literacy screening is often not performed in clinical settings. One possible reason is the concern about the time involved in performing such assessments. Our objective was to measure the time required to administer the Newest Vital Sign (NVS) literacy assessment instrument to English-speaking primary care patients. METHODS: The NVS was administered to 78 consecutive English-speaking patients in an outpatient primary care clinic. The length of time to complete the NVS was timed with a stopwatch. RESULTS: The average time to complete the NVS was 2.9 minutes (95% confidence limit, 2.6-3.1 min). CONCLUSION: The NVS is a health literacy screening tool of sufficient brevity to be considered for use in primary care practices.

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.

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)0.014
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.145
GPT teacher head0.534
Teacher spread0.388 · 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