MétaCan
Menu
← Back to cohort
Record W3080642511 · doi:10.11575/prism/38104

Reflective Function, Maternal-Child Interaction and Child Development: Impacts of Intervention for High Risk Families, Innovative Methods, Measurement and Fidelity Assessment

2020· dissertation· en· W3080642511 on OpenAlexfundno aff
Lubna Anis

Bibliographic record

VenueOpen MIND · 2020
Typedissertation
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
FundersMitacs
KeywordsFidelityIntervention (counseling)Function (biology)Developmental psychologyPsychologyHigh fidelityMedicineComputer scienceEngineeringTelecommunicationsPsychiatryElectrical engineeringBiology

Abstract

fetched live from OpenAlex

Parents suffering from toxic stressors (depression, addictions, family violence) are often unable to respond sensitively to their infants. Such early, persistent stress is understood to interfere with infant brain development, placing infants at risk for health and developmental problems over their lifespan. Parental sensitivity is also influenced by parental Reflective Function (RF), the ability to envision mental states in oneself and one’s child. While many modern parenting programs aim to improve parental sensitivity to their infants to promote healthy child development, parental RF is a commonly missing component. Parental RF is modifiable by intervention and predicts improvements in maternal sensitivity and responsiveness and infant attachment security, thus clearly beneficial. However, the link between an intervention aimed at improving parental RF and child development is unexplored. Given the importance of the early years for children’s development, improved interventions for vulnerable children and families have become public health imperatives. My doctoral research sought to examine the effectiveness of an innovative parenting program called Attachment and Child Health (ATTACHTM) on parent-child interaction and child development. In this manuscript-based dissertation, the first manuscript presents the results from the ATTACHTM pilot studies, demonstrating that ATTACHTM improved outcomes. The ATTACHTM pilots employed new accelerated methods to combat time- and cost-related challenges associated with traditional randomized controlled trials. Therefore, in my second manuscript, I undertook a realist review comparing innovative methods for intervention evaluation with traditional randomized controlled trial (RCT) methods in their ability to test, mobilize knowledge and provide recommendations for best approaches to promote child health. In my third manuscript, I compared the validity of different tools to measure RF, given the increasing need for effective, efficient rapid assessment in wide-ranging settings. Finally, I prepared a manuscript on the need to deliver evidence-based programs to promote early childhood development with fidelity. I developed and assessed an intervention fidelity tool for community nursing research by using the ATTACHTM intervention as an exemplar. My dissertation concludes with a summary of the research findings, and recommendation for nursing research, policy and practice.

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)0.069
metaresearch head score (Gemma)0.125
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.367

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0690.125
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.102
GPT teacher head0.475
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 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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueOpen MIND→Same topicFamily and Disability Support Research→French-language works237,207→