School readiness amongst urban Canadian families: Risk profiles and family mediation.
Bibliographic record
Abstract
There is an ongoing need for literature that identifies the effects of broad contextual risk on school readiness outcomes via family mediating mechanisms. This is especially true amongst diverse and urban samples characterized by variability in immigration history. To address this limitation, family profiles of sociodemographic and contextual risk were identified when children were 2 months of age. Subsequently, their indirect effect on school readiness at 4.5 years was evaluated via family investments and maternal responsivity (N = 501 families). A latent class analysis yielded four distinct family risk profiles: low socioeconomic status (SES) multilevel risk, 12.0% of sample; maternal abuse history, 15.6%; low-SES immigrant risk, 27.7%; and low risk, 44.7%. Path analyses revealed that children in the low-SES multilevel risk and low-SES immigrant risk profiles had the poorest outcomes in all domains and these effects operated equally and indirectly via investments and responsivity. To date, several studies have suggested that sociodemographic risks impact cognitive outcomes primarily via the investment pathway. The present findings suggest that family relations are equally important when operationalized as observed responsive parenting. Furthermore, pathways of influence are similarly operative despite different patterning of adversity for high-risk immigrant and native born families. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".