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Record W4362586125 · doi:10.1016/j.actpsy.2023.103887

Mental health of Canadian children growing up in military families: The parent perspective

2023· article· en· W4362586125 on OpenAlexafffundabout
Ashley Williams, Rachel Richmond, Sarosh Khalid‐Khan, Pappu Reddy, Dianne Groll, Lucia Rühland, Heidi Cramm

Bibliographic record

VenueActa Psychologica · 2023
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsProvidence Health CareRoyal Victoria Regional Health CentreUniversity of TorontoRoyal Victoria HospitalKingston Health Sciences CentreQueen's UniversityMcMaster University
FundersHealth Research Foundation
KeywordsMental healthRelocationPsychological resilienceStressorPsychologyPerspective (graphical)Military personnelContext (archaeology)Vulnerability (computing)Suicide preventionQualitative researchPoison controlPsychiatryMedicineEnvironmental healthSocial psychologyPolitical scienceGeographySociology

Abstract

fetched live from OpenAlex

A recent scoping review found that stressors involved in the military lifestyle (i.e., frequent relocation, parental absence, and risk of parental injury) may be associated with mental health issues among military-children. However, most of the included studies were conducted in the United States with little Canadian representation. To examine the degree to which the scoping review findings are relevant to the Canadian context, we conducted a qualitative study and interviewed 37 parents in Canadian military families. Through the use of a qualitative description approach and content analysis, three themes were identified: 1. Military lifestyle factors have an impact on child mental health; 2. Military life can promote both resilience and vulnerability; and 3. Military lifestyle impacts on parental mental health had an impact on children. These themes align with the scoping review findings asserting that military lifestyle factors can influence child mental health and have significant implications for health care providers working with military-connected children.

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)0.001
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 score0.621
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.359
Teacher spread0.313 · 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
Published2023
Admission routes3
Has abstractyes

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