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Record W2803176682 · doi:10.18357/ijcyfs92201818217

PREPARATION FOR SOCIAL REINTEGRATION AMONG YOUNG GIRLS IN RESIDENTIAL CARE IN INDIA

2018· article· en· W2803176682 on OpenAlexvenueno aff
Satarupa Dutta

Bibliographic record

VenueInternational Journal of Child Youth and Family Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsnot available
Fundersnot available
KeywordsResidential carePsychologyPerceptionIndependence (probability theory)LegislatureFinancial independenceNursingPolitical scienceMedicineBusinessFinance

Abstract

fetched live from OpenAlex

This study focuses on the preparation for social reintegration of young Indian girls about to leave their residential care homes. It assesses the level of preparation by capturing the perception of readiness of 100 girls in institutions: whether they expect to complete higher education, and whether they believe they have acquired such skills as searching for a job, managing finances, problem solving, and maintaining satisfactory relationships. It also explores the impact of different factors, such as the present age of the girls, their self-esteem, and the availability of support networks, on the preparation for their social reintegration. Overall, the findings revealed that the girls felt better prepared with life skills and access to housing after leaving care, but were not so hopeful about their psychological well-being and ability to access higher education, social support, employment, and financial independence. Factors such as age, educational qualifications, self-esteem, and availability of support while in care had a positive relationship with their preparation for social reintegration. Interestingly, the girls’ level of preparation varied significantly across the eight residential care homes studied. The study is intended to help address gaps in the existing literature and to play a significant role in informing future legislative decisions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.551
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.024
GPT teacher head0.353
Teacher spread0.329 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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