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Record W2605294884 · doi:10.3138/jmvfh.4011

Living alongside military PTSD: a qualitative study of female partners’ experiences with UK Veterans

2017· article· en· W2605294884 on OpenAlexvenueno aff
Dominic Murphy, Emily Palmer, Kate Hill, Rachel Ashwick, Walter Busuttil

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2017
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsnot available
FundersRoyal British Legion
KeywordsAmbivalenceMental healthFocus groupPsychologyPsychological interventionQualitative researchHelp-seekingFeelingDistressPeer supportPopulationIsolation (microbiology)Clinical psychologyMedicineSocial psychologyPsychiatrySociology

Abstract

fetched live from OpenAlex

Introduction: An increased risk of emotional burden in partners of military Veterans with mental health difficulties has been observed. This study aimed to explore the experiences and needs of female partners of Veterans seeking help. Methods: Our sample of eight female partners was drawn from a population of help-seeking Veterans who had received treatment for PTSD. Qualitative data were collected using a semi-structured interview schedule. Results: Super-ordinate themes of challenges faced, desired type of support, and barriers to support were each described by a set of sub-themes. Challenges faced were described with the themes of inequality in relationship, loss of congruence with own identity, volatile environment, and emotional distress and isolation. Desired type of support was described by the themes of practical focus on improving, sharing with fellow experts, and support tailored to the partner. Barriers to support were described by the themes feeling restricted by practical barriers and ambivalence about the involvement of others. Discussion: Interventions to support partners of Veterans with mental health difficulties need to address their individual needs, focus on practical techniques, and consider practical limitations.

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)0.003
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 score0.326
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.190
GPT teacher head0.494
Teacher spread0.304 · 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

Citations35
Published2017
Admission routes1
Has abstractyes

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