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Record W4400218527 · doi:10.3389/fcomm.2024.1279906

Experiences of Afghan-Canadian language and cultural advisors who served with Canadian forces abroad: an interpretive phenomenological analysis

2024· article· en· W4400218527 on OpenAlexafffundabout
Jean-Michel Mercier, Victoria Carmichael, Gabrielle Dupuis, Sayed Ahmad Zia Mazhari, Yahseer Fatimi, Tim Laidler, Fardous Hosseiny

Bibliographic record

VenueFrontiers in Communication · 2024
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsCanadian Armed ForcesUniversity of British ColumbiaRoyal Ottawa Mental Health Centre
FundersGovernment of CanadaU.S. Department of Veterans Affairs
KeywordsAfghanInterpretative phenomenological analysisPolitical scienceMedia studiesSociologySocial scienceQualitative researchLaw

Abstract

fetched live from OpenAlex

Though much research has been conducted on the potential well-being effects of deployment on armed forces members, a significant gap seems to exist in the literature when it comes to its effect on conflict-zone interpreters. Drawing on the experiences of six former Afghan-Canadian Language and Cultural Advisors (LCAs), this paper aims to contribute to expanding the nascent literature on conflict-zone interpreters by exploring how former LCAs perceive their experiences before, during, and after their deployment and the resulting impacts on their well-being. Interested in an in-depth exploration of the experiences of former LCAs, this study employed an interpretive phenomenological analysis (IPA) approach. Through the analysis, four superordinate themes emerged in participants’ narratives including: (1) the right opportunity, referring to the reasons for becoming an LCA; (2) overcoming challenges, when it comes to the work itself; (3) deserving better, relating to the experience returning to post-service life; and (4) moving forward, speaking to the current reality of participants. The results reveal key insights into the unique experiences and support needs of former Afghan-Canadian LCAs included in the study, offering an in-depth account of their experience before, during and after their service. The findings also offer important considerations regarding the support available not just to interpreters but to all contractors deployed in conflict-zones.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.322
Teacher spread0.308 · 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 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
Published2024
Admission routes3
Has abstractyes

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