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Record W4304117383 · doi:10.47626/2237-6089-2022-0524

Psychological resilience and mood disorders: a systematic review and meta-analysis

2022· review· en· W4304117383 on OpenAlexafffund
Areeba Imran, Suleman Tariq, Flávio Kapczinski, Taiane de Azevedo Cardoso

Bibliographic record

VenueTrends in Psychiatry and Psychotherapy · 2022
Typereview
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsHamilton Health SciencesMcMaster University
FundersCanadian Institutes of Health ResearchFundação de Amparo à Pesquisa do Estado do Rio Grande do Sul
KeywordsMeta-analysisPsycINFOMoodClinical psychologyPsychological resilienceMood disordersContext (archaeology)Psychological interventionDepression (economics)PsychologySystematic reviewMedicinePsychiatryMEDLINEInternal medicineAnxietyPsychotherapist

Abstract

fetched live from OpenAlex

OBJECTIVE: This systematic review aims to describe the relationship between psychological resilience and mood disorders. METHODS: This is a systematic review and meta-analysis. The following databases were searched on November 6, 2020: PubMed, PsycINFO, and Embase. RESULTS: Twenty-three articles were included and the majority of the studies included (95.7%) showed that psychological resilience has a positive impact in mood disorders. Our meta-analysis showed that individuals with bipolar disorder presented significantly lower levels of psychological resilience compared to controls (standardized mean difference [SDM]: -0.99 [95% confidence interval {95%CI}: -1.13 to -0.85], p < 0.001). In addition, individuals with depression had significantly lower levels of psychological resilience compared to controls (SDM: -0.71 [95%CI -0.81 to -0.61], p < 0.001). CONCLUSION: Our results showed that individuals with mood disorders are less resilient than individuals without mood disorders. Our findings reinforce the importance of investigating interventions that may help to improve psychological resilience considering its positive impact in the context of mood disorders.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.589
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.106
GPT teacher head0.480
Teacher spread0.374 · 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.

Study designMeta-analysis
Domainnot available
GenreReview

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

Citations32
Published2022
Admission routes2
Has abstractyes

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