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Record W3195257980 · doi:10.1177/10497323211039828

Caregiver Support in Mental Health Recovery: A Critical Realist Qualitative Research

2021· article· en· W3195257980 on OpenAlexaffabout
François Lauzier‐Jobin, Janie Houle

Bibliographic record

VenueQualitative Health Research · 2021
Typearticle
Languageen
FieldPsychology
TopicFamily Caregiving in Mental Illness
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsSocial supportMental healthPsychologyThematic analysisQualitative researchInterpersonal communicationAnxietyInterpersonal relationshipIdentification (biology)Clinical psychologySocial psychologyPsychotherapistPsychiatrySociology

Abstract

fetched live from OpenAlex

Support from caregivers is an important element of mental health recovery. However, the mechanisms by which social support influences the recovery of persons with depressive, anxiety, or bipolar disorders are less understood. In this study, we describe the social support mechanisms that influence mental health recovery. A cross-sectional qualitative study was undertaken in Québec (Canada) with 15 persons in recovery and 15 caregivers-those having played the most significant role in their recovery. A deductive thematic analysis allowed for the identification and description of different mechanisms through a triangulation of perspectives from different actors. Regarding classic social support functions, several of the support mechanisms for mental health recovery were identified (emotional support, companionship, instrumental support, and validation). However, informational support was not mentioned. New mechanisms were also identified: presence, communication, and influence. Social support mechanisms evoke a model containing a hierarchy as well as links among them.

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)0.094
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.744
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0940.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0020.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0030.002

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 head0.626
GPT teacher head0.705
Teacher spread0.078 · 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; both teacher heads agree on what is shown here.

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
Published2021
Admission routes2
Has abstractyes

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