Immigrant parents narrate about their encounters with Swedish Healthcare
Bibliographic record
Abstract
Sweden is a multicultural society. Coming to a new country, when having a child with a chronic disease, involves many challenges. One is to approach a new healthcare system. \nThe aim of this study was to gain a broader understanding of immigrant parents’ experiences of their encounters with the Swedish healthcare system.\nMaterials-Methods: Twelve parents of children with asthma were interviewed. The interviews were audio-recorded and transcribed verbatim and then analyzed by using qualitative content analysis \nResults: The results show that immigrant parents’ experiences of Swedish health vary. Some parents are pleased by the care they receive, while others experience a great deal of difficulties. The encounters with the Swedish healthcare system are described as Being met with respect and Affordable care, as well as Problems with communication, Lack of confidence, and Being discriminated against. \nConclusion: Swedish healthcare professionals are expected to offer care on equal terms to the whole population. This is a challenge for healthcare professionals, who need to improve their ability to provide culturally competent care and to understand immigrant parents´ expectations and needs. Such care can be achieved through education of professionals who encounter immigrant parents. Another way is to make it possible for these parents to talk about their expectations and concerns.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".