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Record W4396501831 · doi:10.3138/jmvfh-2023-0075

Involving family members in the care of military personnel and Veterans: A decisional counselling intervention

2024· article· en· W4396501831 on OpenAlexvenueno aff
Angela M. Maguire, Kerri-Ann Woodbury

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsnot available
Fundersnot available
KeywordsIntervention (counseling)Military personnelPsychologyNursingClinical psychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

There are barriers to family-centred care in military-connected families. Practitioners face challenges navigating privacy and confidentiality provisions. Families report difficulties accessing important health-related information from military personnel and Veterans. This article highlights the impacts of informal care on families, identifies at-risk caregivers and families, outlines the family-centred approach to care, and discusses cultural barriers to information sharing in military and Veteran families. A decisional counselling intervention is detailed, which provides practitioners with an informed-consent process for facilitating the sharing of health-related information in military and Veteran families. The intervention is both patient- and family-centred, while managing the medico-legal risks practitioners must navigate in relation to privacy and confidentiality provisions. Given the challenges military and Veteran families face with respect to family-centred care, a pilot study that compares the effectiveness of the proposed decisional counselling intervention with treatment-as-usual is warranted.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0070.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.041
GPT teacher head0.337
Teacher spread0.296 · 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 designNon-randomized trial
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

Citations1
Published2024
Admission routes1
Has abstractyes

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