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Record W4399674619 · doi:10.2196/preprints.57146

Canadian Armed Forces Veterans’ Perspectives on the Effects of Exposure to Children in Armed Conflict During Military Service: Protocol for a Qualitative Study (Preprint)

2024· preprint· en· W4399674619 on OpenAlexaffabout
Catherine Baillie Abidi, San Patten, Stephanie A. Houle, Ken Hoffer, Kathryn Reeves, Stéphanie A.H. Bélanger, Anthony Nazarov, Samantha Wells

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsMcMaster UniversityWestern UniversityDalhousie UniversityRoyal Military College of CanadaVeterans Affairs CanadaCanadian Institute for Military and Veteran Health ResearchCentre for Addiction and Mental HealthMount Saint Vincent University
Fundersnot available
KeywordsMilitary serviceMental healthMilitary personnelSoftware deploymentQualitative researchPsychologyParticipatory action researchMedicinePolitical sciencePublic relationsNursingPsychiatryEngineeringSociologyLawSocial science

Abstract

fetched live from OpenAlex

BACKGROUND The mental health of military personnel has garnered increased attention over the last few decades; however, the impacts of perpetuating, observing, or failing to prevent acts that transgress deeply held moral standards, referred to as moral injuries, are less understood, particularly in relation to encounters with children during deployment. This paper describes a multiphased research protocol that centers around the lived experiences of Canadian Armed Forces (CAF) Veterans to understand how encounters with children during military deployments impact the well-being and mental health of military personnel. OBJECTIVE This study has four objectives: (1) highlight the lived experiences of CAF Veterans who encountered children during military deployments; (2) improve understanding of the nature of experiences that military personnel faced that related to observing or engaging with children during military service; (3) improve understanding of the mental health impacts of encountering children during military service; and (4) use participatory action research (PAR) to develop recommendations for improving preparation, training, and support for military personnel deployed to contexts where encounters with children are likely. METHODS The research project has 2 main phases where phase 1 includes qualitative interviews with CAF Veterans who encountered children during military deployments and phase 2 uses PAR to actively engage Canadian Veterans with lived experiences of encountering children during military deployments, as well as health professionals and researchers to identify recommendations to better address the mental health effects of these encounters. RESULTS As of January 26, 2024, a total of 55 participants and research partners have participated in the 2 phases of the research project. A total of 16 CAF Veterans participated in phase 1 (qualitative interviews), and 39 CAF Veterans, health professionals, and researchers participated in phase 2 (PAR). The results for phase 1 have been finalized and are accepted for publication. Data collection and analysis are ongoing for phase 2. CONCLUSIONS Prioritizing and valuing the experiences of CAF Veterans has deepened our understanding of the intricate nature and impacts of potentially morally injurious events involving children during military deployments. Together with health professionals and researchers, the PAR approach empowers CAF Veterans to articulate important recommendations for developing and improving training and mental health support. This support is crucial not only during the deployment cycle but also throughout the military career, helping lessen the effects of moral injury among military personnel. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/57146

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)0.038
metaresearch head score (Gemma)0.037
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.647
Threshold uncertainty score0.701

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.037
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0050.007
Science and technology studies0.0150.005
Scholarly communication0.0060.003
Open science0.0060.004
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0630.006

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 head0.069
GPT teacher head0.451
Teacher spread0.382 · 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
GenreProtocol

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 routes2
Has abstractyes

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