Exploring barriers and facilitators to digital health literacy among immigrant mothers in Quebec City
Bibliographic record
Abstract
The shift to a digitalized health system brings particular challenges for immigrant mothers who experience a triple adaptation: becoming a mother, navigating a new healthcare system and using technology. This study aims to explore digital health literacy barriers perceived by immigrant mothers and facilitators that could sustain their empowerment related to their health and that of their family. An ethnographic study based on the socioecological model was used to analyze data collected from three individual interviews and two focus groups. The results of the interviews and focus groups allowed us to understand the cultural impact of the lack of digital literacy in the context of healthcare for immigrant mothers in their host country, as well as the barriers and facilitators to access and use digital health information. This study identifies individual, organizational and global level barriers to accessing digital health services amongst immigrant mothers. It also highlighted potential strategies that could support their empowerment in accessing and using digital health resources for their health and that of their family. For instance, training and coaching to help immigrant mothers navigate the health care system are needed. It is also important to adapt public policies to better support the integration of immigrant families in their host country.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".