The effect of educational attainment levels on use of non-traditional health information resources: Findings from the Canadian survey of experiences with primary health care
Bibliographic record
Abstract
Canadian provincial governments have made significant investments in nurse advice telephone lines and Internet resources as non-traditional options to reduce emergency department visits and improve access to health care for the population. However, little is known about the characteristics of users of these services, and who chooses to use them first, before accessing other sources of health advice. Additionally, individuals with lower levels of education tend to be late adopters of technology and have inconsistent utilization of health services. The purpose of the study is to examine the effect of educational attainment levels on the use of non-traditional health information sources first, before other more conventional sources of health information. The study utilized Canadian Survey of Experiences with Primary Health Care (CSE-PHC), 2007-2008 survey data. Logistic regression models were constructed to examine the relationship between use of non-traditional health information sources first, and educational attainment, adjusted for confounders. Relative to someone with less than secondary education, individuals with secondary education (OR = 4.30, 95% CI: 2.44 – 7.59), and individuals with post-secondary education (OR 4.91, 95% CI: 2.78 – 8.67), had significantly greater odds of using non-traditional health information sources first. These findings suggest that educational attainment has a significant effect on the use of non-traditional health information sources first. Future providers of non-traditional health information sources, especially in the design of future eHealth tools and consideration of eHealth literacy, should consider these results in development and implementation of their communications strategies to maximize the reach of their services.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".