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Record W4289261946 · doi:10.21203/rs.3.rs-1891863/v1

Association between Childhood Trauma and Medication Adherence among Patients with Major Depressive Disorder: The Moderating Role of Resilience

2022· preprint· en· W4289261946 on OpenAlexaff
Hongqiong Wang, Yuhua Liao, Lan Guo, Huimin Zhang, Yingli Zhang, Wenjian Lai, Kayla M. Teopiz, Weidong Song, Dongjian Zhu, Lingjiang Li, Ciyong Lu, Beifang Fan, Roger S. McIntyre

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCTQ treeMedicineMajor depressive disorderPsychiatryDepression (economics)Clinical psychologySuicidal ideationPoison controlInjury preventionMoodDomestic violence

Abstract

fetched live from OpenAlex

Abstract Background Medication adherence among patients with major depressive disorder (MDD) is a major and modifiable problem for health care systems. Childhood trauma is considered a vital factor that might be helpful to the early risk assessment of suboptimal adherence. We aimed to comprehensively explore the associations between different types of childhood trauma and medication adherence among patients with MDD, and to test whether measure of resilience has moderating effects on the foregoing associations. Methods Participants were from the Depression Cohort in China (ChiCTR registry number 1900022145), 282 patients with MDD completed the 12 weeks study. The diagnosis of MDD was assessed by trained psychiatrists using the Mini-International Neuropsychiatric Interview (M.I.N.I.). Childhood trauma was evaluated using the Childhood Trauma Questionnaire-28 item Short Form (CTQ-SF), resilience was evaluated using the Connor-Davidson Resilience Scale (CD-RISC). Demographic characteristics, depression symptoms, anxiety symptoms, suicidal ideation, suicidal attempt, insomnia symptoms, and painful somatic symptoms were also investigated. Participants were divided into groups of optimal and suboptimal adherence based on their scores on the Medication Adherence Rating Scale, and factors associated with medication adherence using univariate and multivariate logistic regression, and stratified analyses were evaluated. Results A total of 234 participants (83%) reported suboptimal medication adherence. After adjusting for covariates, CTQ total scores (AOR = 1.04, 95%CI = 1.01–1.06), CTQ measures of sexual abuse (AOR = 1.23, 95%CI = 1.07–1.42), and CTQ measures of physical neglect (AOR = 1.14, 95%CI = 1.04–1.12) were all associated with an increased likelihood of suboptimal adherence. There were significant moderating effects of resilience on the associations of childhood trauma (P = 0.039) and physical neglect (P = 0.034) with medication adherence. The stratification analyses showed that CTQ total scores and CTQ measures of physical neglect were independently associated with an increased risk of suboptimal adherence among patients with MDD with low-resilience or medium-resilience, while not significantly associated with suboptimal adherence in those with high-resilience. Conclusion Obtaining a history of childhood trauma and assessing resilience may help identify patients with suboptimal adherence when providing MDD pharmacotherapy. Psychiatrists may consider enhancing resilience to cope with the adverse effects of childhood trauma on medication adherence.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.423
Teacher spread0.377 · 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 designObservational
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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Citations2
Published2022
Admission routes1
Has abstractyes

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