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Record W1591929658 · doi:10.2196/resprot.3655

Feasibility of Using a Multilingual Web Survey in Studying the Health of Ethnic Minority Youth

2015· article· en· W1591929658 on OpenAlexvenueno aff
Jaana M. Kinnunen, Maili Malin, Susanna Raisamo, Pirjo Lindfors, Lasse Pere, Arja Rimpelä

Bibliographic record

VenueJMIR Research Protocols · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSurvey Methodology and Nonresponse
Canadian institutionsnot available
FundersPirkanmaan Sairaanhoitopiiri
KeywordsEthnic groupHealth equityPsychologyWorld Wide WebComputer sciencePublic healthMedicineSociologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Monolingual Web survey is a common tool for studying adolescent health. However, national languages may cause difficulties for some immigrant-origin youths, which lower their participation rate. In national surveys, the number of ethnic minority groups is often too small to assess their well-being. OBJECTIVE: We studied the feasibility of a multilingual Web survey targeted at immigrant-origin youths by selection of response language, and compared participation in different language groups with a monolingual survey. METHODS: The Adolescent Health and Lifestyle Survey (AHLS), Finland, with national languages (Finnish/Swedish) was modified into a multilingual Web survey targeted at a representative sample of 14- and 16-year olds (N=639) whose registry-based mother tongue was other than the national languages. The survey was conducted in 2010 (16-year olds) and 2011 (14-year olds). The response rate of the multilingual survey in 2011 is compared with the AHLS of 2011. We also describe the translation process and the e-form modification. RESULTS: Of the respondents, 57.6% answered in Finnish, whereas the remaining 42.4% used their mother tongue (P=.002). A majority of youth speaking Somali, Middle Eastern, Albanian, and Southeast Asian languages chose Finnish. The overall response rate was 48.7% with some nonsignificant variation between the language groups. The response rate in the multilingual Web survey was higher (51.6%, 163/316) than the survey with national languages (46.5%, 40/86) in the same age group; however, the difference was not significant (P=.47). The adolescents who had lived in Finland for 5 years or less (58.0%, 102/176) had a higher response rate than those having lived in Finland for more than 5 years (45.1%, 209/463; P=.005). Respondents and nonrespondents did not differ according to place of birth (Finland/other) or residential area (capital city area/other). The difference in the response rates of girls and boys was nearly significant (P=.06). Girls of the Somali and Middle Eastern language groups were underrepresented among the respondents. CONCLUSIONS: A multilingual Web survey is a feasible method for gathering data from ethnic youth, although it does not necessarily yield a higher response rate than a monolingual survey. The respondents answered more often in the official language of the host country than their mother tongue. The varying response rates by time of residence, ethnicity, and gender pose challenges for developing tempting surveys for youth.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.375
metaresearch head score (Gemma)0.073
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.3750.073
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.955
GPT teacher head0.741
Teacher spread0.213 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2015
Admission routes1
Has abstractyes

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