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Record W4300572258 · doi:10.1080/21635781.2022.2098882

Supporting Resilience in Military Families – from Research to Practice

2022· article· en· W4300572258 on OpenAlexaffabout
Lynda Manser, Laurie Ogilvie

Bibliographic record

VenueMilitary Behavioral Health · 2022
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsCanadian Armed Forces
Fundersnot available
KeywordsRelocationPsychological resilienceMental healthMilitary personnelMilitary psychologyModernization theoryPsychologyCombat stress reactionFace (sociological concept)WelfareResilience (materials science)Family resiliencePublic relationsMeaning (existential)Political scienceSociologySocial psychologyPsychiatryLawPsychotherapistComputer science

Abstract

fetched live from OpenAlex

Like many Canadian families, military families deal with struggles around financial stress, intimate partner relationships, mental health, and personal well-being. But military families also face challenges that are altogether unique to the military lifestyle: relocation due to operational postings, repeated absences due to military requirements and taskings, and risk of injury and death. Quite often the combination of these challenges compound each other. Moreover, many of these uniquely military challenges are systemic and repetitive – meaning families will be faced with them again and again. Resilience, the ability to bounce back from adversity, can help families navigate these repetitive and systemic challenges by learning to adapt to changing environments quickly and positively. Military Family Services, a division of Canadian Forces Morale and Welfare Services, relies on research as the grounding for modernization of services to better support the resilience of Canadian military and Veteran families.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.108
GPT teacher head0.542
Teacher spread0.434 · 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.

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

Citations9
Published2022
Admission routes2
Has abstractyes

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