Qualitative assessments of injury prevention strategies among newcomers to Canada
Bibliographic record
Abstract
Background Information Little is known about immigrant children's lifestyles and the uptake of strategies to prevent unintentional injuries. Being an immigrant to Canada can be a risk factor for a child's health because of the cultural differences in the uptake of healthy activities between their country of origin and North America. Objectives To determine the barriers and enablers related to injury prevention strategies such as car seats, bicycle helmet use and participation in formal or informal physical activities. Study Participants Twenty parents from two language groups; Bengali and Spanish, who arrived in Canada within the past 2 years, participated in this study. Participants were recruited with the help of immigrant and settlement organisations. Data Analysis The focus groups were each 2 h and were transcribed and translated into English. Data were analysed using thematic content analysis, which explores emerging themes from interview or focus group data. Result Parents expressed a need for more immigrant friendly injury prevention information. Such information has to be linguistically correct and culturally appropriate for the specific immigrant group. Inadequate information regarding injury prevention and a sense of feeling blamed when their child got hurt were important themes for both groups. Conclusion Immigrant parents want to follow Canadian guidelines for injury prevention, but often do not understand the Canadian cultural context. Healthcare professionals should provide important information programs designed specifically for this growing immigrant population.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".