Bibliographic record
Abstract
Background: Indo-Asians in Canada are at increased risk for cardiovascular diseases. There is a need for cultural and language specific educational materials relating to this risk. During this project we developed and field tested the acceptability of a hypertension public education pamphlet tailored to fit the needs of an at risk local Indo-Asian population, in Calgary, Alberta, Canada. Methods: A community health board representing Calgary’s Indo-Asian communities identified the culturally specific educational needs and language preferences of the local population. An adaptation of an existing English language Canadian Public Hypertension Recommendations pamphlet was created considering the literacy and translation challenges. The adapted pamphlet was translated into four Indo-Asian languages. The adapted pamphlets were disseminated as part of the initial educational component of a community-based culturally and language-sensitive cardiovascular risk factor screening and management program. Field testing of the materials was undertaken when participants returned for program follow-up seven to 12 months later. Results: Fifty-nine English-speaking participants evaluated and confirmed the concept validity of the English adapted version. 28 non-English speaking participants evaluated the Gujarati (N = 13) and Punjabi (N = 15) translated versions of the adapted pamphlets. All participants found the pamphlets acceptable and felt they had
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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.598 | 0.390 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".