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Record W3024337478 · doi:10.3138/jmvfh-2018-0040

Adapting group interpersonal psychotherapy (IPT-G) for treating depression among military spouses at Naval Medical Center Portsmouth (NMCP): Formative qualitative phase

2020· article· en· W3024337478 on OpenAlexvenueno aff
Dalal Alhomaizi, Helen Verdeli, John A. Van Slyke, Katharine Keenan, Cheryl Yunn Shee Foo, Alaa Alhomaizi, Arielle Jean-Pierre, Jennifer Chienwen Kao, Jennifer Shippy, Gail H. Manos

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthPsychological interventionInterpersonal psychotherapyMilitary personnelPsychosocialPsychiatryPopulationThematic analysisMedicinePsychologyQualitative researchRandomized controlled trialEnvironmental health

Abstract

fetched live from OpenAlex

Introduction: Operation Enduring Freedom (OEF) in Afghanistan and Operation Iraqi Freedom (OIF) in Iraq have resulted in the deployment of nearly 2,000,000 troops, of which nearly 60% were married, and nearly half had dependent children. While great attention is being paid to the mental health of returning Veterans, we cannot neglect the mental health of this substantial population of military family members. Studies have found that spouses exhibit similar rates of mental health problems as soldiers returning from combat. Whereas rates of anxiety have been shown to drop significantly post-deployment, depression in military spouses appears to persist following deployment. Although a number of psychosocial interventions tailored to military families have been developed, to the knowledge of the authors, no evidence-based interventions have been adapted to specifically target clinical depression in military spouses. Methods: This case study is part of a larger pilot study that sought to adapt, test, and evaluate Group Interpersonal Therapy (IPT-G), an evidence-based treatment for depression, for depressed military spouses. A formative qualitative assessment is crucial to the intervention’s long-term effectiveness, dissemination, and sustainability. This study aimed to understand military spouses’ unique mental health needs and their experience with mental health services. Three focus groups were conducted – two groups of military spouses and a group of mental health care providers – and transcripts were generated using verbatim note-taking. Five independent coders then coded the transcripts for themes that emerged as most salient using an inductive thematic analysis approach. Results: The results identified were clustered under three main themes: (1) psychosocial stressors for depressed military spouses; (2) barriers to mental health care for military spouses; and (3) proposed services. Discussion: When implementing an intervention for a specific population, optimization of its fit to the needs, priorities, and help-seeking patterns of the population should take place to ensure that it is meaningful, ecologically valid, and sensitive to context and culture. Our analysis showed that the military culture presents unique psychosocial stressors, barriers, demands, and needs to mental health provision that should be accounted for in the adaptation of evidence-based mental health intervention. The interpersonal nature of many of the challenges faced by military spouses lend themselves readily to the problem areas that are treatment targets of IPT, therefore increasing the patients’ potential for engagement and sense of compatibility with the treatment.

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)0.018
metaresearch head score (Gemma)0.018
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0020.001
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.093
GPT teacher head0.474
Teacher spread0.381 · 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
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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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