Canadian adaptation of the Newest Vital Sign<sup>©</sup>, a health literacy assessment tool
Bibliographic record
Abstract
OBJECTIVE: The Newest Vital Sign© (NVS) was developed in the USA to measure patient health literacy in clinical settings. We adapted the NVS for use in Canada, in English and French, and created a computerized version. Our objective was to evaluate the reliability of the Canadian NVS as a self-administered computerized tool. DESIGN: We used a randomized crossover design with a washout period of 3-4 weeks to compare health literacy scores obtained using the computerized version with scores obtained using the standard interviewer-administered NVS. ANOVA models and McNemar's tests assessed differences in outcomes assessed with each version of the NVS and order effects of the testing. SETTING: Participants were recruited from multicultural catchment areas in Ontario and Nova Scotia. SUBJECTS: English- and French-speaking adults aged 18 years or older. RESULTS: A total of 180 (81 %) of the 222 adults (112 English/110 French) initially recruited completed both the interviewer-NVS and computer-NVS. Scores for those who completed both assessments ranged from 0 to 6 with a mean of 3·63 (sd 2·11) for the computerized NVS and 3·41 (sd 2·21) for the interview-administered NVS. Few (n 18; seven English, eleven French) participants' health literacy assessments differed between the two versions. CONCLUSIONS: Overall, the computerized Canadian NVS performed as well as the interviewer-administered version for assessing health literacy levels of English- and French-speaking participants. This Canadian adaptation of the NVS provides Canadian researchers and public health practitioners with an easily administered health literacy assessment tool that can be used to address the needs of Canadians across health literacy levels and ultimately improve health outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".