Health-wellness resources on Canadian immigrant service provider organizations’ websites: A content, navigability, usability, and credibility analysis towards service & asset mapping
Bibliographic record
Abstract
Background: Immigrant service provider organizations (SPOs) are often immigrants' first point of contact to Canadian systems, such as job, education, health and social care, and housing. Prior research emphasizes the health literacy potential of websites as information infrastructures that can reduce information poverty and improve health outcomes. Yet, whether health-wellness resources are present on immigrant SPOs' websites in a user-friendly manner remains unexplored. Methods: We identified the presence of health-wellness resources on SPOs' websites and analyzed those contents to understand their typology. We also ascertained the navigability, usability, and credibility of those websites regarding the health-wellness resources. Results: Among the 1453 SPO websites identified, only 289 (35.9%) had health-wellness information in their web-contents. Of the websites with health-wellness contents, "lifestyle and wellness resources" were present on 86.5% and "healthcare system resources" were present on 80.6% of the websites. Regarding "navigability", zero to two mouse clicks were required to access health-wellness resources on 94.8% of the websites; however, more than one language option was very limited, available on less than a quarter of websites. Conclusions: As immigrants continue to seek information online, immigrant SPOs' websites hold value in increasing the health literacy and health-wellness of immigrants. This research assessed the current state of immigrant SPOs' websites as information infrastructures and reveals areas for improvement. We recommend SPOs add resources for obtaining healthcare card, accessing primary care, sexual and reproductive, parenting, senior's health, mental wellbeing, and women's health information to their websites. We also recommend websites accommodate ethnic language option to improve navigability for immigrants.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 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".