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Record W3093689808 · doi:10.1177/1074840720964397

Young Adults and Their Families Living With Mental Illness: Evaluation of the Usefulness of Family-Centered Support Conversations in Community Mental Health care Settings

2020· article· en· W3093689808 on OpenAlexaboutno aff
Lisbeth Kjelsrud Aass, Hege Skundberg‐Kletthagen, Agneta Schröder, Øyfrid Larsen Moen

Bibliographic record

VenueJournal of Family Nursing · 2020
Typearticle
Languageen
FieldPsychology
TopicFamily Caregiving in Mental Illness
Canadian institutionsnot available
Fundersnot available
KeywordsPhenomenographyMental healthMental illnessIntervention (counseling)Context (archaeology)Everyday lifePsychologyMedicineNursingPsychiatry

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate the usefulness of Family-Centered Support Conversations (FCSC) offered in community mental health care in Norway to young adults and their families experiencing mental illness. The FCSC is a family nursing intervention based on the Calgary Family Assessment and Intervention Models and the Illness Beliefs Model and is focused on how family members can be supportive to each other, how to identify strengths and resources of the family, and how to share and reflect on the experiences of everyday life together while living with mental illness. Interviews were conducted with young adults and their family members in Norway who had received the FCSC intervention and were analyzed using phenomenography. Two descriptive categories were identified: "Facilitating the sharing of reflections about everyday life" and "Possibility of change in everyday life." The family nursing conversations about family structure and function in the context of mental illness allowed families to find new meanings and possibilities in everyday life. Health care professionals can play an important role in facilitating a safe environment for young adults and their families to talk openly about the experience of living with and managing mental illness.

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)0.002
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 score0.261
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.324
Teacher spread0.272 · 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

Citations24
Published2020
Admission routes1
Has abstractyes

Explore more

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