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Two‐generation preschool programme: immediate and 7‐year‐old outcomes for low‐income children and their parents

2012· article· en· W1954579063 on OpenAlexaff
Karen Benzies, Richelle Mychasiuk, Jana Kurilova, Suzanne Tough, Nancy Edwards, Carlene Donnelly

Bibliographic record

VenueChild & Family Social Work · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversity of OttawaUniversity of Calgary
Fundersnot available
KeywordsPsychosocialIntervention (counseling)PsychologyTest (biology)Low incomeSingle parentDevelopmental psychologyMedicineSocioeconomicsPsychiatry

Abstract

fetched live from OpenAlex

Abstract Preschool children living in low‐income families are at increased risk for poor outcomes; early intervention programmes mitigate these risks. While there is considerable evidence of the effectiveness of centre‐based programmes in other jurisdictions, there is limited research about Canadian programmes, specifically programmes that include children and parents. The purpose of this study was to evaluate a single‐site, two‐generation preschool demonstration programme for low‐income families in Canada. A single group, pre‐test (programme intake) /post‐test (programme exit) design with a 7‐year‐old follow‐up was used. Between intake and exit, significant improvements in receptive language and global development were found among the children, and significant improvements in self‐esteem, use of community resources, parenting stress and risk for child maltreatment were found among the parents. These positive improvements were sustained until the children were 7 years old. Public investment in two‐generation preschool programmes may mitigate risks for suboptimal child development and improve parental psychosocial outcomes.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.022
GPT teacher head0.289
Teacher spread0.267 · 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

Citations17
Published2012
Admission routes1
Has abstractyes

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