Health literacy in Toronto : applying the TOFHLA to identify the gap between physician and patient
Bibliographic record
Abstract
This study takes a modern approach applying the TOFHLA to Torontonians in order to identify some of the contributing factors impacting the physician-patient divide. \nThe TOFHLA questionnaire with added customized pre-screening questions was administered to 100 participants who were directly approached, further using a snowball sampling method. \nThe Test of Functional Health Literacy Assessment (TOFHLA) is used to assess a patient’s level of comprehension of health-related material. The TOFHLA was validated by researchers Baker and Parker et al. in two separate studies in 1995 and 1999. \nThis study has proven that age, gender, and English as a first or second language has no effect on health literacy level (P>0.05), education (P=0.024) was the main variable involved with positive health literacy levels. This study has successfully outlined areas of improvement such as, patient experience and engagement which influences recovery time.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".