MétaCan
Menu
Back to cohort

Immigrant parents narrate about their encounters with Swedish Healthcare

2014· article· en· W23531010 on OpenAlexfundno aff
Ingela Rydström

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Educational Sciences
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsImmigrationMulticulturalismHealth careSociologyGender studiesPolitical scienceMedicinePedagogy

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0270.006

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.

Opus teacher head0.103
GPT teacher head0.377
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of the New York Academy of SciencesSame topicSocial and Educational SciencesFrench-language works237,207