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Record W3191309837 · doi:10.32920/ihtp.v1i2.1421

Migrants’ wellbeing and use of information and communication technologies.

2021· article· en· W3191309837 on OpenAlexaffvenue
Jordana Salma, Lalita Kaewwilai, Savera Aziz Ali

Bibliographic record

VenueInternational Health Trends and Perspectives · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInformation and Communications TechnologyICTSPsychological interventionSocial mediaSociologyPublic relationsPolitical sciencePsychology

Abstract

fetched live from OpenAlex

The number of migrants is increasing worldwide coupled with an ever-expanding entrenchment of information and communication technologies (ICTs) in the fabric of daily life. There has been little attention in the health disciplines to the unique ways migrants adopt and are influenced by ICTs across multiple local and transnational social spaces. This scoping review explores the current evidence on migrants’ ICT-mediated transnational social activities and related influences on wellbeing. The review was conducted using Arksey and O'Malley’s (2005) methodological framework and a total of 37 articles were included for the final study. Key findings highlight barriers and facilitators of ICT use in transnational contexts, types of ICT-mediated transnational social activities; and reported influences on migrants’ wellbeing. Migrants’ ICT use facilitates reciprocal channels of social support and continuation of valued social roles. Social role disruption, unequal exchange of social support, and mismatch between migrants’ expectations around ICT use and that of left-behind communities are some of the negative processes with psychological, social, and emotional consequences identified in the review. Main review conclusions emphasize the need to further explore the quality and intensity of ICT-mediated social influences on migrants’ wellbeing and to incorporate a transnational lens in the design of digital learning interventions targeting vulnerable migrant populations.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.033
GPT teacher head0.344
Teacher spread0.312 · 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 teacher head, not a consensus.

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

Citations6
Published2021
Admission routes2
Has abstractyes

Explore more

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