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Record W3037923451 · doi:10.1037/ort0000446

Adjustment of children using supervised access services: Longitudinal outcomes, multiple perspectives, and correlates.

2020· article· en· W3037923451 on OpenAlexaff
Marie‐Christine Saint‐Jacques, Hans Ivers, Sylvie Drapeau, Annick St-Amand, Marie-Christine Fortin

Bibliographic record

VenueAmerican Journal of Orthopsychiatry · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPsychologyLongitudinal studyClinical psychologyMedicineGerontologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Supervised access services (SAS) allow parents who represent a risk for their children or for the other parent to maintain contact with or exchange custody of their children in the presence of a third person. Even though these services have been designed in the children's best interest, few studies have, until now, documented the evolution in the parents' and children's adjustment or in the quality of the relationships between the family members throughout the services. To better understand this evolution, a longitudinal study comprising 3 time measures was conducted with 96 parents who were beginning the services. Although the results revealed that the children's evolution was stable all throughout the service trajectory, further analyses showed that the school-age children, and more particularly the boys, experienced a high level of difficulty. Furthermore, the study showed that the parents' evaluation of their children's adjustment differed according to whether or not the parent had to see the child in a supervised context. Finally, we observed that the evolution of the children's adjustment was correlated with the parents' psychological distress, the quality of the parent-child relationship, and conflict between the parents, all processes that did not have a tendency to improve throughout the service trajectory. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.304
Teacher spread0.276 · 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 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".

Quick stats

Citations5
Published2020
Admission routes1
Has abstractyes

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