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Record W2606673024 · doi:10.1017/s0954579417000177

Proof of concept of a mind–mindedness intervention for mothers hospitalized for severe mental illness

2017· article· en· W2606673024 on OpenAlexaff
Robin Schacht, Elizabeth Meins, Charles Fernyhough, Luna C. Muñoz Centifanti, Jean‐François Bureau, Susan Pawlby

Bibliographic record

VenueDevelopment and Psychopathology · 2017
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsUniversity of Ottawa
FundersEconomic and Social Research Council
KeywordsIntervention (counseling)PsychologySession (web analytics)Mental illnessVideo feedbackClinical psychologyPsychiatryDevelopmental psychologyMental health

Abstract

fetched live from OpenAlex

Studies 1 and 2 investigated how maternal severe mental illness (SMI) related to mothers' mind-mindedness (appropriate and nonattuned mind-related comments). Study 1 showed that mothers with SMI (n = 50) scored lower than psychologically well mothers for both appropriate and nonattuned comments, whereas mothers with SMI in Study 2 (n = 22) had elevated levels of nonattuned comments. Study 2 also tested the efficacy of a single-session video-feedback intervention to facilitate mind-mindedness in mothers with SMI. The intervention was associated with a decrease in nonattuned comments, such that on discharge, mothers did not differ from psychologically well controls. Study 3 assessed infant-mother attachment security in a small subset of intervention-group mothers from Study 2 (n = 9) and a separate group of standard care mothers (n = 30) at infant mean age 17.1 months (SD = 2.1). Infants whose mothers completed the intervention were more likely to be securely attached and less likely to be classified as insecure-disorganized than those of mothers who received standard care. We conclude that a single session of video-feedback to facilitate mind-mindedness in mothers with SMI may have benefits for mother-infant interaction into the second year of life.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.345
Teacher spread0.314 · 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 designOther design
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

Citations58
Published2017
Admission routes1
Has abstractyes

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